大语言模型

以下是 Xinference 中内置的 LLM 列表:

MODEL NAME	ABILITIES	COTNEXT_LENGTH	DESCRIPTION
baichuan-2	generate	4096	Baichuan2 is an open-source Transformer based LLM that is trained on both Chinese and English data.
baichuan-2-chat	chat	4096	Baichuan2-chat is a fine-tuned version of the Baichuan LLM, specializing in chatting.
baichuan-m2	chat, reasoning, hybrid, tools	131072	Baichuan-M2-32B is Baichuan AI's medical-enhanced reasoning model, the second medical model released by Baichuan. Designed for real-world medical reasoning tasks, this model builds upon Qwen2.5-32B with an innovative Large Verifier System. Through domain-specific fine-tuning on real-world medical questions, it achieves breakthrough medical performance while maintaining strong general capabilities.
code-llama	generate	100000	Code-Llama is an open-source LLM trained by fine-tuning LLaMA2 for generating and discussing code.
code-llama-instruct	chat	100000	Code-Llama-Instruct is an instruct-tuned version of the Code-Llama LLM.
code-llama-python	generate	100000	Code-Llama-Python is a fine-tuned version of the Code-Llama LLM, specializing in Python.
codegeex4	chat	131072	the open-source version of the latest CodeGeeX4 model series
codeqwen1.5	generate	65536	CodeQwen1.5 is the Code-Specific version of Qwen1.5. It is a transformer-based decoder-only language model pretrained on a large amount of data of codes.
codeqwen1.5-chat	chat	65536	CodeQwen1.5 is the Code-Specific version of Qwen1.5. It is a transformer-based decoder-only language model pretrained on a large amount of data of codes.
codeshell	generate	8194	CodeShell is a multi-language code LLM developed by the Knowledge Computing Lab of Peking University.
codeshell-chat	chat	8194	CodeShell is a multi-language code LLM developed by the Knowledge Computing Lab of Peking University.
codestral-v0.1	generate	32768	Codestrall-22B-v0.1 is trained on a diverse dataset of 80+ programming languages, including the most popular ones, such as Python, Java, C, C++, JavaScript, and Bash
cogagent	chat, vision	4096	The CogAgent-9B-20241220 model is based on GLM-4V-9B, a bilingual open-source VLM base model. Through data collection and optimization, multi-stage training, and strategy improvements, CogAgent-9B-20241220 achieves significant advancements in GUI perception, inference prediction accuracy, action space completeness, and task generalizability.
deepseek	generate	4096	DeepSeek LLM, trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese.
deepseek-chat	chat	4096	DeepSeek LLM is an advanced language model comprising 67 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese.
deepseek-coder	generate	16384	Deepseek Coder is composed of a series of code language models, each trained from scratch on 2T tokens, with a composition of 87% code and 13% natural language in both English and Chinese.
deepseek-coder-instruct	chat	16384	deepseek-coder-instruct is a model initialized from deepseek-coder-base and fine-tuned on 2B tokens of instruction data.
deepseek-prover-v2	chat, reasoning	163840	We introduce DeepSeek-Prover-V2, an open-source large language model designed for formal theorem proving in Lean 4, with initialization data collected through a recursive theorem proving pipeline powered by DeepSeek-V3. The cold-start training procedure begins by prompting DeepSeek-V3 to decompose complex problems into a series of subgoals. The proofs of resolved subgoals are synthesized into a chain-of-thought process, combined with DeepSeek-V3's step-by-step reasoning, to create an initial cold start for reinforcement learning. This process enables us to integrate both informal and formal mathematical reasoning into a unified model
deepseek-r1	chat, reasoning	163840	DeepSeek-R1, which incorporates cold-start data before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks.
deepseek-r1-0528	chat, reasoning, tools	163840	DeepSeek-R1, which incorporates cold-start data before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks.
deepseek-r1-0528-qwen3	chat, reasoning	131072	The DeepSeek R1 model has undergone a minor version upgrade, with the current version being DeepSeek-R1-0528. In the latest update, DeepSeek R1 has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of leading models, such as O3 and Gemini 2.5 Pro
deepseek-r1-distill-llama	chat, reasoning	131072	deepseek-r1-distill-llama is distilled from DeepSeek-R1 based on Llama
deepseek-r1-distill-qwen	chat, reasoning	131072	deepseek-r1-distill-qwen is distilled from DeepSeek-R1 based on Qwen
deepseek-v2-chat	chat	128000	DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference.
deepseek-v2-chat-0628	chat	128000	DeepSeek-V2-Chat-0628 is an improved version of DeepSeek-V2-Chat.
deepseek-v2.5	chat	128000	DeepSeek-V2.5 is an upgraded version that combines DeepSeek-V2-Chat and DeepSeek-Coder-V2-Instruct. The new model integrates the general and coding abilities of the two previous versions.
deepseek-v3	chat	163840	DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token.
deepseek-v3-0324	chat	163840	DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token.
deepseek-v3.1	chat, reasoning, hybrid, tools	131072	DeepSeek-V3.1 is a hybrid model that supports both thinking mode and non-thinking mode.
deepseek-v3.2	chat, reasoning, hybrid, tools	163840	We introduce DeepSeek-V3.2, a model that harmonizes high computational efficiency with superior reasoning and agent performance
deepseek-v3.2-exp	chat, reasoning, hybrid, tools	163840	We are excited to announce the official release of DeepSeek-V3.2-Exp, an experimental version of our model. As an intermediate step toward our next-generation architecture, V3.2-Exp builds upon V3.1-Terminus by introducing DeepSeek Sparse Attention—a sparse attention mechanism designed to explore and validate optimizations for training and inference efficiency in long-context scenarios.
deepseek-vl2	chat, vision	4096	DeepSeek-VL2, an advanced series of large Mixture-of-Experts (MoE) Vision-Language Models that significantly improves upon its predecessor, DeepSeek-VL. DeepSeek-VL2 demonstrates superior capabilities across various tasks, including but not limited to visual question answering, optical character recognition, document/table/chart understanding, and visual grounding.
dianjin-r1	chat, tools	32768	Tongyi DianJin is a financial intelligence solution platform built by Alibaba Cloud, dedicated to providing financial business developers with a convenient artificial intelligence application development environment.
ernie4.5	chat	131072	ERNIE 4.5, a new family of large-scale multimodal models comprising 10 distinct variants.
fin-r1	chat	131072	Fin-R1 is a large language model specifically designed for the field of financial reasoning
gemma-3-1b-it	chat	32768	Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models.
gemma-3-it	chat, vision	131072	Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models.
gemma-4	generate, chat, reasoning, audio, vision, hybrid	262144	Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on small models) and generating text output.
glm-4.1v-thinking	chat, vision, reasoning, tools	65536	GLM-4.1V-9B-Thinking, designed to explore the upper limits of reasoning in vision-language models.
glm-4.5	chat, reasoning, hybrid, tools	131072	The GLM-4.5 series models are foundation models designed for intelligent agents.
glm-4.5v	chat, vision, reasoning, tools	131072	GLM-4.5V is based on ZhipuAI’s next-generation flagship text foundation model GLM-4.5-Air (106B parameters, 12B active). It continues the technical approach of GLM-4.1V-Thinking, achieving SOTA performance among models of the same scale on 42 public vision-language benchmarks.
glm-4.6	chat, reasoning, hybrid, tools	202752	GLM-4.6 significantly enhances context length (up to 200K tokens), code generation, reasoning with tool use, agent capabilities, and human-aligned writing compared to GLM-4.5.
glm-4.7	chat, reasoning, hybrid, tools	202752	GLM-4.7 significantly advances core and multilingual agentic coding, UI/vibe coding, tool use, and complex reasoning—outperforming GLM-4.6 across benchmarks like SWE-bench, Terminal Bench 2.0, τ²-Bench, and HLE—while also improving chat, creative writing, and role-play.
glm-4.7-flash	chat, reasoning, hybrid, tools	202752	GLM-4.7-Flash is a 30B-A3B MoE model. As the strongest model in the 30B class, it offers a lightweight deployment option that balances performance and efficiency.
glm-4v	chat, vision	8192	GLM4 is the open source version of the latest generation of pre-trained models in the GLM-4 series launched by Zhipu AI.
glm-5	chat, vision, tools, reasoning	202752	We are launching GLM-5, targeting complex systems engineering and long-horizon agentic tasks. Scaling is still one of the most important ways to improve the intelligence efficiency of Artificial General Intelligence (AGI). Compared to GLM-4.5, GLM-5 scales from 355B parameters (32B active) to 744B parameters (40B active), and increases pre-training data from 23T to 28.5T tokens. GLM-5 also integrates DeepSeek Sparse Attention (DSA), largely reducing deployment cost while preserving long-context capacity. Reinforcement learning aims to bridge the gap between competence and excellence in pre-trained models. However, deploying it at scale for LLMs is a challenge due to the RL training inefficiency. To this end, we developed slime, a novel asynchronous RL infrastructure that substantially improves training throughput and efficiency, enabling more fine-grained post-training iterations. With advances in both pre-training and post-training, GLM-5 delivers significant improvement compared to GLM-4.7 across a wide range of academic benchmarks and achieves best-in-class performance among all open-source models in the world on reasoning, coding, and agentic tasks, closing the gap with frontier models.
glm-5.1	chat, vision, tools, reasoning, hybrid	202752	GLM-5.1 is our next-generation flagship model for agentic engineering, with significantly stronger coding capabilities than its predecessor. It achieves state-of-the-art performance on SWE-Bench Pro and leads GLM-5 by a wide margin on NL2Repo (repo generation) and Terminal-Bench 2.0 (real-world terminal tasks).
glm-edge-chat	chat	8192	The GLM-Edge series is our attempt to face the end-side real-life scenarios, which consists of two sizes of large-language dialogue models and multimodal comprehension models (GLM-Edge-1.5B-Chat, GLM-Edge-4B-Chat, GLM-Edge-V-2B, GLM-Edge-V-5B). Among them, the 1.5B / 2B model is mainly for platforms such as mobile phones and cars, and the 4B / 5B model is mainly for platforms such as PCs.
glm4-0414	chat, tools	32768	The GLM family welcomes new members, the GLM-4-32B-0414 series models, featuring 32 billion parameters. Its performance is comparable to OpenAI’s GPT series and DeepSeek’s V3/R1 series
glm4-chat	chat, tools	131072	GLM4 is the open source version of the latest generation of pre-trained models in the GLM-4 series launched by Zhipu AI.
glm4-chat-1m	chat, tools	1048576	GLM4 is the open source version of the latest generation of pre-trained models in the GLM-4 series launched by Zhipu AI.
gorilla-openfunctions-v2	chat	4096	OpenFunctions is designed to extend Large Language Model (LLM) Chat Completion feature to formulate executable APIs call given natural language instructions and API context.
gpt-2	generate	1024	GPT-2 is a Transformer-based LLM that is trained on WebTest, a 40 GB dataset of Reddit posts with 3+ upvotes.
gpt-oss	chat, reasoning	131072	gpt-oss series, OpenAI’s open-weight models designed for powerful reasoning, agentic tasks, and versatile developer use cases.
huatuogpt-o1-llama-3.1	chat, tools	131072	HuatuoGPT-o1 is a medical LLM designed for advanced medical reasoning. It generates a complex thought process, reflecting and refining its reasoning, before providing a final response.
huatuogpt-o1-qwen2.5	chat, tools	32768	HuatuoGPT-o1 is a medical LLM designed for advanced medical reasoning. It generates a complex thought process, reflecting and refining its reasoning, before providing a final response.
internlm3-instruct	chat, tools	32768	InternLM3 has open-sourced an 8-billion parameter instruction model, InternLM3-8B-Instruct, designed for general-purpose usage and advanced reasoning.
internvl3	chat, vision	8192	InternVL3, an advanced multimodal large language model (MLLM) series that demonstrates superior overall performance.
kat-v1	chat	131072	Kwaipilot-AutoThink ranks first among all open-source models on LiveCodeBench Pro, a challenging benchmark explicitly designed to prevent data leakage, and even surpasses strong proprietary systems such as Seed and o3-mini.
kimi-k2.5	chat, vision	262144	Kimi K2.5 is an open-source, native multimodal agentic model built through continual pretraining on approximately 15 trillion mixed visual and text tokens atop Kimi-K2-Base. It seamlessly integrates vision and language understanding with advanced agentic capabilities, instant and thinking modes, as well as conversational and agentic paradigms.
llama-2	generate	4096	Llama-2 is the second generation of Llama, open-source and trained on a larger amount of data.
llama-2-chat	chat	4096	Llama-2-Chat is a fine-tuned version of the Llama-2 LLM, specializing in chatting.
llama-3	generate	8192	Llama 3 is an auto-regressive language model that uses an optimized transformer architecture
llama-3-instruct	chat	8192	The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks..
llama-3.1	generate	131072	Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture
llama-3.1-instruct	chat, tools	131072	The Llama 3.1 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks..
llama-3.2-vision	generate, vision	131072	The Llama 3.2-Vision instruction-tuned models are optimized for visual recognition, image reasoning, captioning, and answering general questions about an image...
llama-3.2-vision-instruct	chat, vision	131072	Llama 3.2-Vision instruction-tuned models are optimized for visual recognition, image reasoning, captioning, and answering general questions about an image...
llama-3.3-instruct	chat, tools	131072	The Llama 3.3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks..
marco-o1	chat, tools	32768	Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions
mineru2.5-2509-1.2b	chat, vision	32768	MinerU2.5-2509-1.2B is a vision language model for document understanding.
minicpm-2b-dpo-bf16	chat	4096	MiniCPM is an End-Size LLM developed by ModelBest Inc. and TsinghuaNLP, with only 2.4B parameters excluding embeddings.
minicpm-2b-dpo-fp16	chat	4096	MiniCPM is an End-Size LLM developed by ModelBest Inc. and TsinghuaNLP, with only 2.4B parameters excluding embeddings.
minicpm-2b-dpo-fp32	chat	4096	MiniCPM is an End-Size LLM developed by ModelBest Inc. and TsinghuaNLP, with only 2.4B parameters excluding embeddings.
minicpm-2b-sft-bf16	chat	4096	MiniCPM is an End-Size LLM developed by ModelBest Inc. and TsinghuaNLP, with only 2.4B parameters excluding embeddings.
minicpm-2b-sft-fp32	chat	4096	MiniCPM is an End-Size LLM developed by ModelBest Inc. and TsinghuaNLP, with only 2.4B parameters excluding embeddings.
minicpm-v-2.6	chat, vision	32768	MiniCPM-V 2.6 is the latest model in the MiniCPM-V series. The model is built on SigLip-400M and Qwen2-7B with a total of 8B parameters.
minicpm-v-4.5	chat, vision	32768	MiniCPM-V 4.5 is an improved version in the MiniCPM-V series with enhanced multimodal capabilities and better performance.
minicpm3-4b	chat	32768	MiniCPM3-4B is the 3rd generation of MiniCPM series. The overall performance of MiniCPM3-4B surpasses Phi-3.5-mini-Instruct and GPT-3.5-Turbo-0125, being comparable with many recent 7B~9B models.
minicpm4	chat	32768	MiniCPM4 series are highly efficient large language models (LLMs) designed explicitly for end-side devices, which achieves this efficiency through systematic innovation in four key dimensions: model architecture, training data, training algorithms, and inference systems.
minimax-m2	chat, tools, reasoning	196608	MiniMax-M2, a Mini model built for Max coding & agentic workflows.
minimax-m2.5	chat, tools, reasoning	196608	MiniMax-M2.5, a Mini model built for Max coding & agentic workflows.
minimax-m2.7	chat, tools, reasoning, hybrid	204800	MiniMax-M2.7 is our first model deeply participating in its own evolution. M2.7 is capable of building complex agent harnesses and completing highly elaborate productivity tasks, leveraging Agent Teams, complex Skills, and dynamic tool search
mistral-instruct-v0.1	chat	8192	Mistral-7B-Instruct is a fine-tuned version of the Mistral-7B LLM on public datasets, specializing in chatting.
mistral-instruct-v0.2	chat	8192	The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an improved instruct fine-tuned version of Mistral-7B-Instruct-v0.1.
mistral-instruct-v0.3	chat	32768	The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an improved instruct fine-tuned version of Mistral-7B-Instruct-v0.1.
mistral-large-instruct	chat	131072	Mistral-Large-Instruct-2407 is an advanced dense Large Language Model (LLM) of 123B parameters with state-of-the-art reasoning, knowledge and coding capabilities.
mistral-nemo-instruct	chat	1024000	The Mistral-Nemo-Instruct-2407 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-Nemo-Base-2407
mistral-v0.1	generate	8192	Mistral-7B is a unmoderated Transformer based LLM claiming to outperform Llama2 on all benchmarks.
mixtral-8x22b-instruct-v0.1	chat	65536	The Mixtral-8x22B-Instruct-v0.1 Large Language Model (LLM) is an instruct fine-tuned version of the Mixtral-8x22B-v0.1, specializing in chatting.
mixtral-instruct-v0.1	chat	32768	Mistral-8x7B-Instruct is a fine-tuned version of the Mistral-8x7B LLM, specializing in chatting.
mixtral-v0.1	generate	32768	The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts.
moonlight-16b-a3b-instruct	chat	8192	Kimi Muon is Scalable for LLM Training
openhermes-2.5	chat	8192	Openhermes 2.5 is a fine-tuned version of Mistral-7B-v0.1 on primarily GPT-4 generated data.
opt	generate	2048	Opt is an open-source, decoder-only, Transformer based LLM that was designed to replicate GPT-3.
orion-chat	chat	4096	Orion-14B series models are open-source multilingual large language models trained from scratch by OrionStarAI.
ovis2	chat, vision	32768	Ovis (Open VISion) is a novel Multimodal Large Language Model (MLLM) architecture, designed to structurally align visual and textual embeddings.
phi-2	generate	2048	Phi-2 is a 2.7B Transformer based LLM used for research on model safety, trained with data similar to Phi-1.5 but augmented with synthetic texts and curated websites.
phi-3-mini-128k-instruct	chat	128000	The Phi-3-Mini-128K-Instruct is a 3.8 billion-parameter, lightweight, state-of-the-art open model trained using the Phi-3 datasets.
phi-3-mini-4k-instruct	chat	4096	The Phi-3-Mini-4k-Instruct is a 3.8 billion-parameter, lightweight, state-of-the-art open model trained using the Phi-3 datasets.
qvq-72b-preview	chat, vision	32768	QVQ-72B-Preview is an experimental research model developed by the Qwen team, focusing on enhancing visual reasoning capabilities.
qwen-chat	chat	32768	Qwen-chat is a fine-tuned version of the Qwen LLM trained with alignment techniques, specializing in chatting.
qwen1.5-chat	chat, tools	32768	Qwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data.
qwen1.5-moe-chat	chat, tools	32768	Qwen1.5-MoE is a transformer-based MoE decoder-only language model pretrained on a large amount of data.
qwen2-audio-instruct	chat, audio	32768	Qwen2-Audio: A large-scale audio-language model which is capable of accepting various audio signal inputs and performing audio analysis or direct textual responses with regard to speech instructions.
qwen2-instruct	chat, tools	32768	Qwen2 is the new series of Qwen large language models
qwen2-moe-instruct	chat, tools	32768	Qwen2 is the new series of Qwen large language models.
qwen2-vl-instruct	chat, vision	32768	Qwen2-VL: To See the World More Clearly.Qwen2-VL is the latest version of the vision language models in the Qwen model familities.
qwen2.5	generate	32768	Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters.
qwen2.5-coder	generate	32768	Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen).
qwen2.5-coder-instruct	chat, tools	32768	Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen).
qwen2.5-instruct	chat, tools	32768	Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters.
qwen2.5-instruct-1m	chat	1010000	Qwen2.5-1M is the long-context version of the Qwen2.5 series models, supporting a context length of up to 1M tokens.
qwen2.5-omni	chat, vision, audio, omni	32768	Qwen2.5-Omni: the new flagship end-to-end multimodal model in the Qwen series.
qwen2.5-vl-instruct	chat, vision	128000	Qwen2.5-VL: Qwen2.5-VL is the latest version of the vision language models in the Qwen model familities.
qwen3	chat, reasoning, hybrid, tools	40960	Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support.
qwen3-coder	chat, tools	262144	we're announcing Qwen3-Coder, our most agentic code model to date
qwen3-instruct	chat, tools	262144	We introduce the updated version of the Qwen3-235B-A22B non-thinking mode, named Qwen3-235B-A22B-Instruct-2507
qwen3-next-instruct	chat, tools	262144	Qwen3-Next-80B-A3B is the first installment in the Qwen3-Next series
qwen3-next-thinking	chat, reasoning, tools	262144	Qwen3-Next-80B-A3B is the first installment in the Qwen3-Next series
qwen3-omni-instruct	chat, vision, audio, omni, tools	262144	Qwen3-Omni is the natively end-to-end multilingual omni-modal foundation models. It processes text, images, audio, and video, and delivers real-time streaming responses in both text and natural speech. We introduce several architectural upgrades to improve performance and efficiency.
qwen3-omni-thinking	chat, vision, audio, omni, reasoning, tools	262144	Qwen3-Omni is the natively end-to-end multilingual omni-modal foundation models. It processes text, images, audio, and video, and delivers real-time streaming responses in both text and natural speech. We introduce several architectural upgrades to improve performance and efficiency.
qwen3-thinking	chat, reasoning, tools	262144	we have continued to scale the thinking capability of Qwen3-235B-A22B, improving both the quality and depth of reasoning
qwen3-vl-instruct	chat, vision, tools	262144	Meet Qwen3-VL — the most powerful vision-language model in the Qwen series to date.
qwen3-vl-thinking	chat, vision, reasoning, tools	262144	Meet Qwen3-VL — the most powerful vision-language model in the Qwen series to date.
qwen3.5	chat, vision, tools, reasoning, hybrid	262144	Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency.
qwen3.6	chat, vision, tools, reasoning, hybrid	262144	Following the February release of the Qwen3.5 series, we're pleased to share the first open-weight variant of Qwen3.6. Built on direct feedback from the community, Qwen3.6 prioritizes stability and real-world utility, offering developers a more intuitive, responsive, and genuinely productive coding experience.
qwenlong-l1	chat	32768	QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning
qwq-32b	chat, reasoning, tools	131072	QwQ is the reasoning model of the Qwen series. Compared with conventional instruction-tuned models, QwQ, which is capable of thinking and reasoning, can achieve significantly enhanced performance in downstream tasks, especially hard problems. QwQ-32B is the medium-sized reasoning model, which is capable of achieving competitive performance against state-of-the-art reasoning models, e.g., DeepSeek-R1, o1-mini.
qwq-32b-preview	chat	32768	QwQ-32B-Preview is an experimental research model developed by the Qwen Team, focused on advancing AI reasoning capabilities.
seallm_v2	generate	8192	We introduce SeaLLM-7B-v2, the state-of-the-art multilingual LLM for Southeast Asian (SEA) languages
seallm_v2.5	generate	8192	We introduce SeaLLM-7B-v2.5, the state-of-the-art multilingual LLM for Southeast Asian (SEA) languages
seallms-v3	chat	32768	SeaLLMs - Large Language Models for Southeast Asia
seed-oss	chat, reasoning, tools	524288	Seed-OSS is a series of open-source large language models developed by ByteDance's Seed Team, designed for powerful long-context, reasoning, agent and general capabilities, and versatile developer-friendly features. Although trained with only 12T tokens, Seed-OSS achieves excellent performance on several popular open benchmarks.
skywork	generate	4096	Skywork is a series of large models developed by the Kunlun Group · Skywork team.
skywork-math	generate	4096	Skywork is a series of large models developed by the Kunlun Group · Skywork team.
skywork-or1	chat	131072	We release the final version of Skywork-OR1 (Open Reasoner 1) series of models, including
skywork-or1-preview	chat	32768	The Skywork-OR1 (Open Reasoner 1) model series consists of powerful math and code reasoning models trained using large-scale rule-based reinforcement learning with carefully designed datasets and training recipes.
telechat	chat	8192	The TeleChat is a large language model developed and trained by China Telecom Artificial Intelligence Technology Co., LTD. The 7B model base is trained with 1.5 trillion Tokens and 3 trillion Tokens and Chinese high-quality corpus.
tiny-llama	generate	2048	The TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens.
wizardcoder-python-v1.0	chat	100000
wizardmath-v1.0	chat	2048	WizardMath is an open-source LLM trained by fine-tuning Llama2 with Evol-Instruct, specializing in math.
xiyansql-qwencoder-2504	chat, tools	32768	The XiYanSQL-QwenCoder models, as multi-dialect SQL base models, demonstrating robust SQL generation capabilities.
xverse	generate	2048	XVERSE is a multilingual large language model, independently developed by Shenzhen Yuanxiang Technology.
xverse-chat	chat	2048	XVERSEB-Chat is the aligned version of model XVERSE.
yi	generate	4096	The Yi series models are large language models trained from scratch by developers at 01.AI.
yi-1.5	generate	4096	Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples.
yi-1.5-chat	chat	4096	Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples.
yi-1.5-chat-16k	chat	16384	Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples.
yi-200k	generate	262144	The Yi series models are large language models trained from scratch by developers at 01.AI.
yi-chat	chat	4096	The Yi series models are large language models trained from scratch by developers at 01.AI.

• baichuan-2
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (pytorch, 13 Billion)
• baichuan-2-chat
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (pytorch, 13 Billion)
• Baichuan-M2
  • Specifications
    • Model Spec 1 (pytorch, 32 Billion)
    • Model Spec 2 (gptq, 32 Billion)
• code-llama
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (pytorch, 13 Billion)
    • Model Spec 3 (pytorch, 34 Billion)
    • Model Spec 4 (ggufv2, 7 Billion)
    • Model Spec 5 (ggufv2, 13 Billion)
    • Model Spec 6 (ggufv2, 34 Billion)
• code-llama-instruct
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (pytorch, 13 Billion)
    • Model Spec 3 (pytorch, 34 Billion)
    • Model Spec 4 (ggufv2, 7 Billion)
    • Model Spec 5 (ggufv2, 13 Billion)
    • Model Spec 6 (ggufv2, 34 Billion)
• code-llama-python
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (pytorch, 13 Billion)
    • Model Spec 3 (pytorch, 34 Billion)
    • Model Spec 4 (ggufv2, 7 Billion)
    • Model Spec 5 (ggufv2, 13 Billion)
    • Model Spec 6 (ggufv2, 34 Billion)
• codegeex4
  • Specifications
    • Model Spec 1 (pytorch, 9 Billion)
    • Model Spec 2 (ggufv2, 9 Billion)
• codeqwen1.5
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
• codeqwen1.5-chat
  • Specifications
    • Model Spec 1 (ggufv2, 7 Billion)
    • Model Spec 2 (pytorch, 7 Billion)
    • Model Spec 3 (awq, 7 Billion)
• codeshell
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
• codeshell-chat
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
• codestral-v0.1
  • Specifications
    • Model Spec 1 (pytorch, 22 Billion)
    • Model Spec 2 (ggufv2, 22 Billion)
    • Model Spec 3 (mlx, 22 Billion)
    • Model Spec 4 (mlx, 22 Billion)
• cogagent
  • Specifications
    • Model Spec 1 (pytorch, 9 Billion)
• deepseek
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (pytorch, 67 Billion)
    • Model Spec 3 (ggufv2, 7 Billion)
    • Model Spec 4 (ggufv2, 67 Billion)
• deepseek-chat
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (pytorch, 67 Billion)
    • Model Spec 3 (ggufv2, 7 Billion)
    • Model Spec 4 (ggufv2, 67 Billion)
• deepseek-coder
  • Specifications
    • Model Spec 1 (pytorch, 1_3 Billion)
    • Model Spec 2 (pytorch, 6_7 Billion)
    • Model Spec 3 (pytorch, 7 Billion)
    • Model Spec 4 (pytorch, 33 Billion)
    • Model Spec 5 (ggufv2, 1_3 Billion)
    • Model Spec 6 (ggufv2, 6_7 Billion)
    • Model Spec 7 (ggufv2, 7 Billion)
    • Model Spec 8 (ggufv2, 33 Billion)
    • Model Spec 9 (gptq, 1_3 Billion)
    • Model Spec 10 (gptq, 6_7 Billion)
    • Model Spec 11 (gptq, 33 Billion)
    • Model Spec 12 (awq, 1_3 Billion)
    • Model Spec 13 (awq, 6_7 Billion)
    • Model Spec 14 (awq, 33 Billion)
• deepseek-coder-instruct
  • Specifications
    • Model Spec 1 (pytorch, 1_3 Billion)
    • Model Spec 2 (pytorch, 6_7 Billion)
    • Model Spec 3 (pytorch, 7 Billion)
    • Model Spec 4 (pytorch, 33 Billion)
    • Model Spec 5 (ggufv2, 1_3 Billion)
    • Model Spec 6 (ggufv2, 6_7 Billion)
    • Model Spec 7 (ggufv2, 7 Billion)
    • Model Spec 8 (ggufv2, 33 Billion)
    • Model Spec 9 (gptq, 1_3 Billion)
    • Model Spec 10 (gptq, 6_7 Billion)
    • Model Spec 11 (gptq, 33 Billion)
    • Model Spec 12 (awq, 1_3 Billion)
    • Model Spec 13 (awq, 6_7 Billion)
    • Model Spec 14 (awq, 33 Billion)
• deepseek-prover-v2
  • Specifications
    • Model Spec 1 (pytorch, 671 Billion)
    • Model Spec 2 (pytorch, 7 Billion)
    • Model Spec 3 (mlx, 7 Billion)
• deepseek-r1
  • Specifications
    • Model Spec 1 (pytorch, 671 Billion)
    • Model Spec 2 (awq, 671 Billion)
    • Model Spec 3 (ggufv2, 671 Billion)
    • Model Spec 4 (mlx, 671 Billion)
• deepseek-r1-0528
  • Specifications
    • Model Spec 1 (pytorch, 671 Billion)
    • Model Spec 2 (gptq, 671 Billion)
• deepseek-r1-0528-qwen3
  • Specifications
    • Model Spec 1 (pytorch, 8 Billion)
    • Model Spec 2 (gptq, 8 Billion)
    • Model Spec 3 (gptq, 8 Billion)
• deepseek-r1-distill-llama
  • Specifications
    • Model Spec 1 (pytorch, 8 Billion)
    • Model Spec 2 (mlx, 8 Billion)
    • Model Spec 3 (pytorch, 70 Billion)
    • Model Spec 4 (ggufv2, 70 Billion)
    • Model Spec 5 (mlx, 70 Billion)
    • Model Spec 6 (awq, 8 Billion)
    • Model Spec 7 (gptq, 8 Billion)
    • Model Spec 8 (ggufv2, 1_5 Billion)
    • Model Spec 9 (awq, 70 Billion)
    • Model Spec 10 (gptq, 70 Billion)
    • Model Spec 11 (ggufv2, 8 Billion)
• deepseek-r1-distill-qwen
  • Specifications
    • Model Spec 1 (pytorch, 1_5 Billion)
    • Model Spec 2 (ggufv2, 1_5 Billion)
    • Model Spec 3 (mlx, 1_5 Billion)
    • Model Spec 4 (pytorch, 7 Billion)
    • Model Spec 5 (gptq, 7 Billion)
    • Model Spec 6 (ggufv2, 7 Billion)
    • Model Spec 7 (pytorch, 14 Billion)
    • Model Spec 8 (ggufv2, 14 Billion)
    • Model Spec 9 (pytorch, 32 Billion)
    • Model Spec 10 (ggufv2, 32 Billion)
    • Model Spec 11 (awq, 1_5 Billion)
    • Model Spec 12 (gptq, 1_5 Billion)
    • Model Spec 13 (awq, 7 Billion)
    • Model Spec 14 (mlx, 7 Billion)
    • Model Spec 15 (awq, 14 Billion)
    • Model Spec 16 (mlx, 14 Billion)
    • Model Spec 17 (awq, 32 Billion)
    • Model Spec 18 (mlx, 32 Billion)
    • Model Spec 19 (gptq, 32 Billion)
• deepseek-v2-chat
  • Specifications
    • Model Spec 1 (pytorch, 16 Billion)
    • Model Spec 2 (pytorch, 236 Billion)
• deepseek-v2-chat-0628
  • Specifications
    • Model Spec 1 (pytorch, 236 Billion)
• deepseek-v2.5
  • Specifications
    • Model Spec 1 (pytorch, 236 Billion)
• deepseek-v3
  • Specifications
    • Model Spec 1 (pytorch, 671 Billion)
    • Model Spec 2 (awq, 671 Billion)
    • Model Spec 3 (ggufv2, 671 Billion)
    • Model Spec 4 (mlx, 671 Billion)
• deepseek-v3-0324
  • Specifications
    • Model Spec 1 (pytorch, 671 Billion)
    • Model Spec 2 (awq, 671 Billion)
    • Model Spec 3 (mlx, 671 Billion)
• Deepseek-V3.1
  • Specifications
    • Model Spec 1 (pytorch, 671 Billion)
    • Model Spec 2 (gptq, 671 Billion)
    • Model Spec 3 (awq, 671 Billion)
    • Model Spec 4 (mlx, 671 Billion)
• DeepSeek-V3.2
  • Specifications
    • Model Spec 1 (pytorch, 671 Billion)
    • Model Spec 2 (awq, 671 Billion)
• DeepSeek-V3.2-Exp
  • Specifications
    • Model Spec 1 (pytorch, 671 Billion)
    • Model Spec 2 (awq, 671 Billion)
• deepseek-vl2
  • Specifications
    • Model Spec 1 (pytorch, 27 Billion)
    • Model Spec 2 (pytorch, 16 Billion)
    • Model Spec 3 (pytorch, 3 Billion)
• DianJin-R1
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (pytorch, 32 Billion)
    • Model Spec 3 (ggufv2, 7 Billion)
    • Model Spec 4 (ggufv2, 7 Billion)
    • Model Spec 5 (ggufv2, 32 Billion)
    • Model Spec 6 (ggufv2, 32 Billion)
• Ernie4.5
  • Specifications
    • Model Spec 1 (pytorch, 0_3 Billion)
    • Model Spec 2 (ggufv2, 0_3 Billion)
    • Model Spec 3 (mlx, 0_3 Billion)
    • Model Spec 4 (pytorch, 21 Billion)
    • Model Spec 5 (ggufv2, 21 Billion)
    • Model Spec 6 (mlx, 21 Billion)
    • Model Spec 7 (pytorch, 300 Billion)
    • Model Spec 8 (ggufv2, 300 Billion)
    • Model Spec 9 (mlx, 300 Billion)
• fin-r1
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (gptq, 7 Billion)
    • Model Spec 3 (fp8, 7 Billion)
• gemma-3-1b-it
  • Specifications
    • Model Spec 1 (pytorch, 1 Billion)
    • Model Spec 2 (mlx, 1 Billion)
    • Model Spec 3 (ggufv2, 1 Billion)
• gemma-3-it
  • Specifications
    • Model Spec 1 (pytorch, 4 Billion)
    • Model Spec 2 (pytorch, 12 Billion)
    • Model Spec 3 (pytorch, 27 Billion)
    • Model Spec 4 (mlx, 4 Billion)
    • Model Spec 5 (mlx, 12 Billion)
    • Model Spec 6 (mlx, 27 Billion)
    • Model Spec 7 (ggufv2, 4 Billion)
    • Model Spec 8 (ggufv2, 12 Billion)
    • Model Spec 9 (ggufv2, 27 Billion)
• gemma-4
  • Specifications
    • Model Spec 1 (pytorch, 2 Billion)
    • Model Spec 2 (pytorch, 4 Billion)
    • Model Spec 3 (pytorch, 31 Billion)
    • Model Spec 4 (fp4, 31 Billion)
    • Model Spec 5 (pytorch, 26 Billion)
    • Model Spec 6 (ggufv2, 2 Billion)
    • Model Spec 7 (ggufv2, 4 Billion)
    • Model Spec 8 (ggufv2, 31 Billion)
    • Model Spec 9 (ggufv2, 26 Billion)
    • Model Spec 10 (mlx, 2 Billion)
    • Model Spec 11 (mlx, 4 Billion)
    • Model Spec 12 (mlx, 31 Billion)
    • Model Spec 13 (mlx, 26 Billion)
• glm-4.1v-thinking
  • Specifications
    • Model Spec 1 (pytorch, 9 Billion)
    • Model Spec 2 (awq, 9 Billion)
    • Model Spec 3 (gptq, 9 Billion)
• glm-4.5
  • Specifications
    • Model Spec 1 (pytorch, 355 Billion)
    • Model Spec 2 (fp8, 355 Billion)
    • Model Spec 3 (gptq, 355 Billion)
    • Model Spec 4 (awq, 355 Billion)
    • Model Spec 5 (mlx, 355 Billion)
    • Model Spec 6 (pytorch, 106 Billion)
    • Model Spec 7 (fp8, 106 Billion)
    • Model Spec 8 (gptq, 106 Billion)
    • Model Spec 9 (awq, 106 Billion)
    • Model Spec 10 (awq, 106 Billion)
    • Model Spec 11 (mlx, 106 Billion)
• glm-4.5v
  • Specifications
    • Model Spec 1 (pytorch, 106 Billion)
    • Model Spec 2 (fp8, 106 Billion)
    • Model Spec 3 (awq, 106 Billion)
    • Model Spec 4 (mlx, 106 Billion)
• GLM-4.6
  • Specifications
    • Model Spec 1 (pytorch, 355 Billion)
    • Model Spec 2 (fp8, 355 Billion)
    • Model Spec 3 (gptq, 355 Billion)
    • Model Spec 4 (awq, 355 Billion)
    • Model Spec 5 (mlx, 355 Billion)
• GLM-4.7
  • Specifications
    • Model Spec 1 (pytorch, 355 Billion)
    • Model Spec 2 (fp8, 355 Billion)
    • Model Spec 3 (gptq, 355 Billion)
    • Model Spec 4 (awq, 355 Billion)
    • Model Spec 5 (mlx, 355 Billion)
• GLM-4.7-Flash
  • Specifications
    • Model Spec 1 (pytorch, 30 Billion)
• glm-4v
  • Specifications
    • Model Spec 1 (pytorch, 9 Billion)
• glm-5
  • Specifications
    • Model Spec 1 (pytorch, 744 Billion)
    • Model Spec 2 (fp8, 744 Billion)
    • Model Spec 3 (ggufv2, 744 Billion)
    • Model Spec 4 (mlx, 744 Billion)
• glm-5.1
  • Specifications
    • Model Spec 1 (pytorch, 744 Billion)
    • Model Spec 2 (fp8, 744 Billion)
    • Model Spec 3 (ggufv2, 744 Billion)
    • Model Spec 4 (mlx, 744 Billion)
• glm-edge-chat
  • Specifications
    • Model Spec 1 (pytorch, 1_5 Billion)
    • Model Spec 2 (pytorch, 4 Billion)
    • Model Spec 3 (ggufv2, 1_5 Billion)
    • Model Spec 4 (ggufv2, 1_5 Billion)
    • Model Spec 5 (ggufv2, 4 Billion)
    • Model Spec 6 (ggufv2, 4 Billion)
• glm4-0414
  • Specifications
    • Model Spec 1 (pytorch, 9 Billion)
    • Model Spec 2 (pytorch, 32 Billion)
    • Model Spec 3 (mlx, 9 Billion)
    • Model Spec 4 (mlx, 32 Billion)
    • Model Spec 5 (ggufv2, 9 Billion)
    • Model Spec 6 (ggufv2, 32 Billion)
• glm4-chat
  • Specifications
    • Model Spec 1 (pytorch, 9 Billion)
    • Model Spec 2 (ggufv2, 9 Billion)
• glm4-chat-1m
  • Specifications
    • Model Spec 1 (pytorch, 9 Billion)
    • Model Spec 2 (ggufv2, 9 Billion)
• gorilla-openfunctions-v2
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (ggufv2, 7 Billion)
• gpt-2
  • Specifications
    • Model Spec 1 (pytorch, 1_5 Billion)
• gpt-oss
  • Specifications
    • Model Spec 1 (pytorch, 20 Billion)
    • Model Spec 2 (bnb, 20 Billion)
    • Model Spec 3 (pytorch, 120 Billion)
• HuatuoGPT-o1-LLaMA-3.1
  • Specifications
    • Model Spec 1 (pytorch, 8 Billion)
    • Model Spec 2 (pytorch, 70 Billion)
• HuatuoGPT-o1-Qwen2.5
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (pytorch, 72 Billion)
• internlm3-instruct
  • Specifications
    • Model Spec 1 (pytorch, 8 Billion)
    • Model Spec 2 (gptq, 8 Billion)
    • Model Spec 3 (awq, 8 Billion)
    • Model Spec 4 (ggufv2, 8 Billion)
    • Model Spec 5 (mlx, 8 Billion)
• InternVL3
  • Specifications
    • Model Spec 1 (pytorch, 1 Billion)
    • Model Spec 2 (awq, 1 Billion)
    • Model Spec 3 (pytorch, 2 Billion)
    • Model Spec 4 (awq, 2 Billion)
    • Model Spec 5 (pytorch, 8 Billion)
    • Model Spec 6 (awq, 8 Billion)
    • Model Spec 7 (pytorch, 9 Billion)
    • Model Spec 8 (awq, 9 Billion)
    • Model Spec 9 (pytorch, 14 Billion)
    • Model Spec 10 (awq, 14 Billion)
    • Model Spec 11 (pytorch, 38 Billion)
    • Model Spec 12 (awq, 38 Billion)
    • Model Spec 13 (pytorch, 78 Billion)
    • Model Spec 14 (awq, 78 Billion)
• KAT-V1
  • Specifications
    • Model Spec 1 (pytorch, 40 Billion)
    • Model Spec 2 (gptq, 40 Billion)
    • Model Spec 3 (awq, 40 Billion)
• Kimi-K2.5
  • Specifications
    • Model Spec 1 (pytorch, 1058_59 Billion)
    • Model Spec 2 (ggufv2, 1058_59 Billion)
    • Model Spec 3 (mlx, 1058_59 Billion)
• llama-2
  • Specifications
    • Model Spec 1 (ggufv2, 7 Billion)
    • Model Spec 2 (gptq, 7 Billion)
    • Model Spec 3 (awq, 7 Billion)
    • Model Spec 4 (ggufv2, 13 Billion)
    • Model Spec 5 (ggufv2, 70 Billion)
    • Model Spec 6 (pytorch, 7 Billion)
    • Model Spec 7 (pytorch, 13 Billion)
    • Model Spec 8 (gptq, 13 Billion)
    • Model Spec 9 (awq, 13 Billion)
    • Model Spec 10 (pytorch, 70 Billion)
    • Model Spec 11 (gptq, 70 Billion)
    • Model Spec 12 (awq, 70 Billion)
• llama-2-chat
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (pytorch, 13 Billion)
    • Model Spec 3 (pytorch, 70 Billion)
    • Model Spec 4 (ggufv2, 7 Billion)
    • Model Spec 5 (ggufv2, 13 Billion)
    • Model Spec 6 (ggufv2, 70 Billion)
    • Model Spec 7 (gptq, 7 Billion)
    • Model Spec 8 (gptq, 70 Billion)
    • Model Spec 9 (awq, 70 Billion)
    • Model Spec 10 (awq, 7 Billion)
    • Model Spec 11 (gptq, 13 Billion)
    • Model Spec 12 (awq, 13 Billion)
• llama-3
  • Specifications
    • Model Spec 1 (pytorch, 8 Billion)
    • Model Spec 2 (ggufv2, 8 Billion)
    • Model Spec 3 (pytorch, 70 Billion)
    • Model Spec 4 (ggufv2, 70 Billion)
• llama-3-instruct
  • Specifications
    • Model Spec 1 (pytorch, 8 Billion)
    • Model Spec 2 (pytorch, 70 Billion)
    • Model Spec 3 (ggufv2, 8 Billion)
    • Model Spec 4 (ggufv2, 70 Billion)
    • Model Spec 5 (mlx, 8 Billion)
    • Model Spec 6 (mlx, 8 Billion)
    • Model Spec 7 (mlx, 8 Billion)
    • Model Spec 8 (mlx, 70 Billion)
    • Model Spec 9 (mlx, 70 Billion)
    • Model Spec 10 (mlx, 70 Billion)
    • Model Spec 11 (gptq, 8 Billion)
    • Model Spec 12 (gptq, 70 Billion)
• llama-3.1
  • Specifications
    • Model Spec 1 (pytorch, 8 Billion)
    • Model Spec 2 (ggufv2, 8 Billion)
    • Model Spec 3 (pytorch, 70 Billion)
    • Model Spec 4 (pytorch, 405 Billion)
• llama-3.1-instruct
  • Specifications
    • Model Spec 1 (pytorch, 8 Billion)
    • Model Spec 2 (pytorch, 8 Billion)
    • Model Spec 3 (gptq, 8 Billion)
    • Model Spec 4 (awq, 8 Billion)
    • Model Spec 5 (pytorch, 70 Billion)
    • Model Spec 6 (pytorch, 70 Billion)
    • Model Spec 7 (gptq, 70 Billion)
    • Model Spec 8 (awq, 70 Billion)
    • Model Spec 9 (pytorch, 405 Billion)
    • Model Spec 10 (gptq, 405 Billion)
    • Model Spec 11 (awq, 405 Billion)
    • Model Spec 12 (ggufv2, 8 Billion)
    • Model Spec 13 (ggufv2, 70 Billion)
    • Model Spec 14 (mlx, 8 Billion)
    • Model Spec 15 (mlx, 8 Billion)
    • Model Spec 16 (mlx, 8 Billion)
    • Model Spec 17 (mlx, 70 Billion)
    • Model Spec 18 (mlx, 70 Billion)
    • Model Spec 19 (mlx, 70 Billion)
• llama-3.2-vision
  • Specifications
    • Model Spec 1 (pytorch, 11 Billion)
    • Model Spec 2 (pytorch, 90 Billion)
• llama-3.2-vision-instruct
  • Specifications
    • Model Spec 1 (pytorch, 11 Billion)
    • Model Spec 2 (pytorch, 90 Billion)
• llama-3.3-instruct
  • Specifications
    • Model Spec 1 (pytorch, 70 Billion)
    • Model Spec 2 (gptq, 70 Billion)
    • Model Spec 3 (awq, 70 Billion)
    • Model Spec 4 (mlx, 70 Billion)
    • Model Spec 5 (ggufv2, 70 Billion)
• marco-o1
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (ggufv2, 7 Billion)
• MinerU2.5-2509-1.2B
  • Specifications
    • Model Spec 1 (pytorch, 1_2 Billion)
• minicpm-2b-dpo-bf16
  • Specifications
    • Model Spec 1 (pytorch, 2 Billion)
• minicpm-2b-dpo-fp16
  • Specifications
    • Model Spec 1 (pytorch, 2 Billion)
• minicpm-2b-dpo-fp32
  • Specifications
    • Model Spec 1 (pytorch, 2 Billion)
• minicpm-2b-sft-bf16
  • Specifications
    • Model Spec 1 (pytorch, 2 Billion)
• minicpm-2b-sft-fp32
  • Specifications
    • Model Spec 1 (pytorch, 2 Billion)
• MiniCPM-V-2.6
  • Specifications
    • Model Spec 1 (pytorch, 8 Billion)
    • Model Spec 2 (pytorch, 8 Billion)
• MiniCPM-V-4.5
  • Specifications
    • Model Spec 1 (pytorch, 8 Billion)
    • Model Spec 2 (pytorch, 8 Billion)
• minicpm3-4b
  • Specifications
    • Model Spec 1 (pytorch, 4 Billion)
    • Model Spec 2 (gptq, 4 Billion)
• minicpm4
  • Specifications
    • Model Spec 1 (pytorch, 0_5 Billion)
    • Model Spec 2 (pytorch, 8 Billion)
    • Model Spec 3 (mlx, 8 Billion)
• MiniMax-M2
  • Specifications
    • Model Spec 1 (pytorch, 230 Billion)
    • Model Spec 2 (awq, 230 Billion)
    • Model Spec 3 (mlx, 230 Billion)
• MiniMax-M2.5
  • Specifications
    • Model Spec 1 (pytorch, 230 Billion)
    • Model Spec 2 (ggufv2, 230 Billion)
    • Model Spec 3 (mlx, 230 Billion)
• MiniMax-M2.7
  • Specifications
    • Model Spec 1 (pytorch, 230 Billion)
    • Model Spec 2 (ggufv2, 230 Billion)
    • Model Spec 3 (mlx, 230 Billion)
• mistral-instruct-v0.1
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (awq, 7 Billion)
    • Model Spec 3 (gptq, 7 Billion)
    • Model Spec 4 (ggufv2, 7 Billion)
• mistral-instruct-v0.2
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (gptq, 7 Billion)
    • Model Spec 3 (awq, 7 Billion)
    • Model Spec 4 (ggufv2, 7 Billion)
• mistral-instruct-v0.3
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (gptq, 7 Billion)
    • Model Spec 3 (awq, 7 Billion)
    • Model Spec 4 (ggufv2, 7 Billion)
• mistral-large-instruct
  • Specifications
    • Model Spec 1 (pytorch, 123 Billion)
    • Model Spec 2 (pytorch, 123 Billion)
    • Model Spec 3 (gptq, 123 Billion)
    • Model Spec 4 (awq, 123 Billion)
    • Model Spec 5 (ggufv2, 123 Billion)
    • Model Spec 6 (mlx, 123 Billion)
    • Model Spec 7 (mlx, 123 Billion)
    • Model Spec 8 (mlx, 123 Billion)
• mistral-nemo-instruct
  • Specifications
    • Model Spec 1 (pytorch, 12 Billion)
    • Model Spec 2 (pytorch, 12 Billion)
    • Model Spec 3 (pytorch, 12 Billion)
    • Model Spec 4 (gptq, 12 Billion)
    • Model Spec 5 (awq, 12 Billion)
    • Model Spec 6 (ggufv2, 12 Billion)
    • Model Spec 7 (mlx, 12 Billion)
    • Model Spec 8 (mlx, 12 Billion)
    • Model Spec 9 (mlx, 12 Billion)
• mistral-v0.1
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (ggufv2, 7 Billion)
• mixtral-8x22B-instruct-v0.1
  • Specifications
    • Model Spec 1 (pytorch, 141 Billion)
    • Model Spec 2 (awq, 141 Billion)
    • Model Spec 3 (gptq, 141 Billion)
    • Model Spec 4 (ggufv2, 141 Billion)
• mixtral-instruct-v0.1
  • Specifications
    • Model Spec 1 (pytorch, 46_7 Billion)
    • Model Spec 2 (awq, 46_7 Billion)
    • Model Spec 3 (gptq, 46_7 Billion)
    • Model Spec 4 (ggufv2, 46_7 Billion)
• mixtral-v0.1
  • Specifications
    • Model Spec 1 (pytorch, 46_7 Billion)
    • Model Spec 2 (gptq, 46_7 Billion)
    • Model Spec 3 (ggufv2, 46_7 Billion)
• moonlight-16b-a3b-instruct
  • Specifications
    • Model Spec 1 (pytorch, 3 Billion)
• openhermes-2.5
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (ggufv2, 7 Billion)
• opt
  • Specifications
    • Model Spec 1 (pytorch, 1 Billion)
• orion-chat
  • Specifications
    • Model Spec 1 (pytorch, 14 Billion)
    • Model Spec 2 (awq, 14 Billion)
• Ovis2
  • Specifications
    • Model Spec 1 (pytorch, 1 Billion)
    • Model Spec 2 (pytorch, 2 Billion)
    • Model Spec 3 (pytorch, 4 Billion)
    • Model Spec 4 (pytorch, 8 Billion)
    • Model Spec 5 (pytorch, 16 Billion)
    • Model Spec 6 (pytorch, 34 Billion)
    • Model Spec 7 (gptq, 2 Billion)
    • Model Spec 8 (gptq, 4 Billion)
    • Model Spec 9 (gptq, 8 Billion)
    • Model Spec 10 (gptq, 16 Billion)
    • Model Spec 11 (gptq, 34 Billion)
• phi-2
  • Specifications
    • Model Spec 1 (ggufv2, 2 Billion)
    • Model Spec 2 (pytorch, 2 Billion)
• phi-3-mini-128k-instruct
  • Specifications
    • Model Spec 1 (pytorch, 4 Billion)
• phi-3-mini-4k-instruct
  • Specifications
    • Model Spec 1 (ggufv2, 4 Billion)
    • Model Spec 2 (pytorch, 4 Billion)
• QvQ-72B-Preview
  • Specifications
    • Model Spec 1 (pytorch, 72 Billion)
    • Model Spec 2 (mlx, 72 Billion)
• qwen-chat
  • Specifications
    • Model Spec 1 (ggufv2, 7 Billion)
    • Model Spec 2 (ggufv2, 14 Billion)
    • Model Spec 3 (pytorch, 1_8 Billion)
    • Model Spec 4 (pytorch, 7 Billion)
    • Model Spec 5 (pytorch, 14 Billion)
    • Model Spec 6 (pytorch, 72 Billion)
    • Model Spec 7 (gptq, 7 Billion)
    • Model Spec 8 (gptq, 1_8 Billion)
    • Model Spec 9 (gptq, 14 Billion)
    • Model Spec 10 (gptq, 72 Billion)
• qwen1.5-chat
  • Specifications
    • Model Spec 1 (pytorch, 0_5 Billion)
    • Model Spec 2 (pytorch, 1_8 Billion)
    • Model Spec 3 (pytorch, 4 Billion)
    • Model Spec 4 (pytorch, 7 Billion)
    • Model Spec 5 (pytorch, 14 Billion)
    • Model Spec 6 (pytorch, 32 Billion)
    • Model Spec 7 (pytorch, 72 Billion)
    • Model Spec 8 (pytorch, 110 Billion)
    • Model Spec 9 (gptq, 0_5 Billion)
    • Model Spec 10 (gptq, 1_8 Billion)
    • Model Spec 11 (gptq, 4 Billion)
    • Model Spec 12 (gptq, 7 Billion)
    • Model Spec 13 (gptq, 14 Billion)
    • Model Spec 14 (gptq, 32 Billion)
    • Model Spec 15 (gptq, 72 Billion)
    • Model Spec 16 (gptq, 110 Billion)
    • Model Spec 17 (awq, 0_5 Billion)
    • Model Spec 18 (awq, 1_8 Billion)
    • Model Spec 19 (awq, 4 Billion)
    • Model Spec 20 (awq, 7 Billion)
    • Model Spec 21 (awq, 14 Billion)
    • Model Spec 22 (awq, 32 Billion)
    • Model Spec 23 (awq, 72 Billion)
    • Model Spec 24 (awq, 110 Billion)
    • Model Spec 25 (ggufv2, 0_5 Billion)
    • Model Spec 26 (ggufv2, 1_8 Billion)
    • Model Spec 27 (ggufv2, 4 Billion)
    • Model Spec 28 (ggufv2, 7 Billion)
    • Model Spec 29 (ggufv2, 14 Billion)
    • Model Spec 30 (ggufv2, 32 Billion)
    • Model Spec 31 (ggufv2, 72 Billion)
• qwen1.5-moe-chat
  • Specifications
    • Model Spec 1 (pytorch, 2_7 Billion)
    • Model Spec 2 (gptq, 2_7 Billion)
• qwen2-audio-instruct
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
• qwen2-instruct
  • Specifications
    • Model Spec 1 (pytorch, 0_5 Billion)
    • Model Spec 2 (pytorch, 1_5 Billion)
    • Model Spec 3 (pytorch, 7 Billion)
    • Model Spec 4 (pytorch, 72 Billion)
    • Model Spec 5 (gptq, 0_5 Billion)
    • Model Spec 6 (gptq, 1_5 Billion)
    • Model Spec 7 (gptq, 7 Billion)
    • Model Spec 8 (gptq, 72 Billion)
    • Model Spec 9 (awq, 0_5 Billion)
    • Model Spec 10 (awq, 1_5 Billion)
    • Model Spec 11 (awq, 7 Billion)
    • Model Spec 12 (awq, 72 Billion)
    • Model Spec 13 (fp8, 0_5 Billion)
    • Model Spec 14 (fp8, 0_5 Billion)
    • Model Spec 15 (fp8, 1_5 Billion)
    • Model Spec 16 (fp8, 7 Billion)
    • Model Spec 17 (fp8, 72 Billion)
    • Model Spec 18 (mlx, 0_5 Billion)
    • Model Spec 19 (mlx, 1_5 Billion)
    • Model Spec 20 (mlx, 7 Billion)
    • Model Spec 21 (mlx, 72 Billion)
    • Model Spec 22 (ggufv2, 0_5 Billion)
    • Model Spec 23 (ggufv2, 1_5 Billion)
    • Model Spec 24 (ggufv2, 7 Billion)
    • Model Spec 25 (ggufv2, 72 Billion)
• qwen2-moe-instruct
  • Specifications
    • Model Spec 1 (pytorch, 14 Billion)
    • Model Spec 2 (gptq, 14 Billion)
    • Model Spec 3 (ggufv2, 14 Billion)
• qwen2-vl-instruct
  • Specifications
    • Model Spec 1 (pytorch, 2 Billion)
    • Model Spec 2 (gptq, 2 Billion)
    • Model Spec 3 (gptq, 2 Billion)
    • Model Spec 4 (awq, 2 Billion)
    • Model Spec 5 (mlx, 2 Billion)
    • Model Spec 6 (pytorch, 7 Billion)
    • Model Spec 7 (gptq, 7 Billion)
    • Model Spec 8 (gptq, 7 Billion)
    • Model Spec 9 (awq, 7 Billion)
    • Model Spec 10 (pytorch, 72 Billion)
    • Model Spec 11 (awq, 72 Billion)
    • Model Spec 12 (gptq, 72 Billion)
    • Model Spec 13 (mlx, 72 Billion)
    • Model Spec 14 (mlx, 7 Billion)
• qwen2.5
  • Specifications
    • Model Spec 1 (pytorch, 0_5 Billion)
    • Model Spec 2 (pytorch, 1_5 Billion)
    • Model Spec 3 (pytorch, 3 Billion)
    • Model Spec 4 (pytorch, 7 Billion)
    • Model Spec 5 (pytorch, 14 Billion)
    • Model Spec 6 (pytorch, 32 Billion)
    • Model Spec 7 (pytorch, 72 Billion)
• qwen2.5-coder
  • Specifications
    • Model Spec 1 (pytorch, 0_5 Billion)
    • Model Spec 2 (pytorch, 1_5 Billion)
    • Model Spec 3 (pytorch, 3 Billion)
    • Model Spec 4 (pytorch, 7 Billion)
    • Model Spec 5 (pytorch, 14 Billion)
    • Model Spec 6 (pytorch, 32 Billion)
• qwen2.5-coder-instruct
  • Specifications
    • Model Spec 1 (pytorch, 0_5 Billion)
    • Model Spec 2 (pytorch, 1_5 Billion)
    • Model Spec 3 (pytorch, 3 Billion)
    • Model Spec 4 (pytorch, 7 Billion)
    • Model Spec 5 (pytorch, 14 Billion)
    • Model Spec 6 (pytorch, 32 Billion)
    • Model Spec 7 (gptq, 0_5 Billion)
    • Model Spec 8 (gptq, 1_5 Billion)
    • Model Spec 9 (awq, 0_5 Billion)
    • Model Spec 10 (awq, 1_5 Billion)
    • Model Spec 11 (ggufv2, 1_5 Billion)
    • Model Spec 12 (ggufv2, 7 Billion)
    • Model Spec 13 (gptq, 3 Billion)
    • Model Spec 14 (gptq, 7 Billion)
    • Model Spec 15 (gptq, 14 Billion)
    • Model Spec 16 (gptq, 32 Billion)
    • Model Spec 17 (awq, 3 Billion)
    • Model Spec 18 (awq, 7 Billion)
    • Model Spec 19 (awq, 14 Billion)
    • Model Spec 20 (awq, 32 Billion)
    • Model Spec 21 (gptq, 3 Billion)
    • Model Spec 22 (gptq, 7 Billion)
    • Model Spec 23 (gptq, 14 Billion)
    • Model Spec 24 (gptq, 32 Billion)
    • Model Spec 25 (awq, 3 Billion)
    • Model Spec 26 (awq, 7 Billion)
    • Model Spec 27 (awq, 14 Billion)
    • Model Spec 28 (awq, 32 Billion)
• qwen2.5-instruct
  • Specifications
    • Model Spec 1 (pytorch, 0_5 Billion)
    • Model Spec 2 (pytorch, 1_5 Billion)
    • Model Spec 3 (pytorch, 3 Billion)
    • Model Spec 4 (pytorch, 7 Billion)
    • Model Spec 5 (pytorch, 14 Billion)
    • Model Spec 6 (pytorch, 32 Billion)
    • Model Spec 7 (pytorch, 72 Billion)
    • Model Spec 8 (gptq, 0_5 Billion)
    • Model Spec 9 (gptq, 1_5 Billion)
    • Model Spec 10 (gptq, 3 Billion)
    • Model Spec 11 (gptq, 7 Billion)
    • Model Spec 12 (gptq, 14 Billion)
    • Model Spec 13 (gptq, 32 Billion)
    • Model Spec 14 (gptq, 72 Billion)
    • Model Spec 15 (awq, 0_5 Billion)
    • Model Spec 16 (awq, 1_5 Billion)
    • Model Spec 17 (awq, 3 Billion)
    • Model Spec 18 (awq, 7 Billion)
    • Model Spec 19 (awq, 14 Billion)
    • Model Spec 20 (awq, 32 Billion)
    • Model Spec 21 (awq, 72 Billion)
    • Model Spec 22 (ggufv2, 0_5 Billion)
    • Model Spec 23 (ggufv2, 1_5 Billion)
    • Model Spec 24 (ggufv2, 3 Billion)
    • Model Spec 25 (ggufv2, 7 Billion)
    • Model Spec 26 (ggufv2, 14 Billion)
    • Model Spec 27 (ggufv2, 32 Billion)
    • Model Spec 28 (mlx, 3 Billion)
    • Model Spec 29 (mlx, 3 Billion)
    • Model Spec 30 (mlx, 7 Billion)
    • Model Spec 31 (mlx, 7 Billion)
    • Model Spec 32 (mlx, 14 Billion)
    • Model Spec 33 (mlx, 14 Billion)
    • Model Spec 34 (mlx, 32 Billion)
    • Model Spec 35 (mlx, 32 Billion)
    • Model Spec 36 (mlx, 72 Billion)
    • Model Spec 37 (mlx, 72 Billion)
    • Model Spec 38 (ggufv2, 72 Billion)
    • Model Spec 39 (mlx, 0_5 Billion)
    • Model Spec 40 (mlx, 0_5 Billion)
    • Model Spec 41 (mlx, 0_5 Billion)
    • Model Spec 42 (mlx, 1_5 Billion)
    • Model Spec 43 (mlx, 1_5 Billion)
    • Model Spec 44 (mlx, 1_5 Billion)
    • Model Spec 45 (mlx, 3 Billion)
    • Model Spec 46 (mlx, 7 Billion)
    • Model Spec 47 (mlx, 14 Billion)
    • Model Spec 48 (mlx, 32 Billion)
    • Model Spec 49 (mlx, 72 Billion)
• qwen2.5-instruct-1m
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (pytorch, 14 Billion)
• qwen2.5-omni
  • Specifications
    • Model Spec 1 (pytorch, 3 Billion)
    • Model Spec 2 (pytorch, 7 Billion)
• qwen2.5-vl-instruct
  • Specifications
    • Model Spec 1 (pytorch, 3 Billion)
    • Model Spec 2 (pytorch, 7 Billion)
    • Model Spec 3 (pytorch, 32 Billion)
    • Model Spec 4 (pytorch, 72 Billion)
    • Model Spec 5 (awq, 3 Billion)
    • Model Spec 6 (awq, 7 Billion)
    • Model Spec 7 (awq, 32 Billion)
    • Model Spec 8 (awq, 72 Billion)
    • Model Spec 9 (mlx, 3 Billion)
    • Model Spec 10 (mlx, 7 Billion)
    • Model Spec 11 (mlx, 32 Billion)
    • Model Spec 12 (mlx, 72 Billion)
• qwen3
  • Specifications
    • Model Spec 1 (pytorch, 0_6 Billion)
    • Model Spec 2 (fp8, 0_6 Billion)
    • Model Spec 3 (gptq, 0_6 Billion)
    • Model Spec 4 (gptq, 0_6 Billion)
    • Model Spec 5 (mlx, 0_6 Billion)
    • Model Spec 6 (ggufv2, 0_6 Billion)
    • Model Spec 7 (pytorch, 1_7 Billion)
    • Model Spec 8 (fp8, 1_7 Billion)
    • Model Spec 9 (gptq, 1_7 Billion)
    • Model Spec 10 (gptq, 1_7 Billion)
    • Model Spec 11 (mlx, 1_7 Billion)
    • Model Spec 12 (ggufv2, 1_7 Billion)
    • Model Spec 13 (pytorch, 4 Billion)
    • Model Spec 14 (fp8, 4 Billion)
    • Model Spec 15 (awq, 4 Billion)
    • Model Spec 16 (gptq, 4 Billion)
    • Model Spec 17 (mlx, 4 Billion)
    • Model Spec 18 (ggufv2, 4 Billion)
    • Model Spec 19 (pytorch, 8 Billion)
    • Model Spec 20 (fp8, 8 Billion)
    • Model Spec 21 (awq, 8 Billion)
    • Model Spec 22 (gptq, 8 Billion)
    • Model Spec 23 (mlx, 8 Billion)
    • Model Spec 24 (ggufv2, 8 Billion)
    • Model Spec 25 (pytorch, 14 Billion)
    • Model Spec 26 (fp8, 14 Billion)
    • Model Spec 27 (awq, 14 Billion)
    • Model Spec 28 (gptq, 14 Billion)
    • Model Spec 29 (mlx, 14 Billion)
    • Model Spec 30 (ggufv2, 14 Billion)
    • Model Spec 31 (pytorch, 30 Billion)
    • Model Spec 32 (fp8, 30 Billion)
    • Model Spec 33 (gptq, 30 Billion)
    • Model Spec 34 (gptq, 30 Billion)
    • Model Spec 35 (mlx, 30 Billion)
    • Model Spec 36 (ggufv2, 30 Billion)
    • Model Spec 37 (pytorch, 32 Billion)
    • Model Spec 38 (fp8, 32 Billion)
    • Model Spec 39 (awq, 32 Billion)
    • Model Spec 40 (gptq, 32 Billion)
    • Model Spec 41 (mlx, 32 Billion)
    • Model Spec 42 (ggufv2, 32 Billion)
    • Model Spec 43 (pytorch, 235 Billion)
    • Model Spec 44 (fp8, 235 Billion)
    • Model Spec 45 (gptq, 235 Billion)
    • Model Spec 46 (gptq, 235 Billion)
    • Model Spec 47 (mlx, 235 Billion)
    • Model Spec 48 (ggufv2, 235 Billion)
• Qwen3-Coder
  • Specifications
    • Model Spec 1 (pytorch, 480 Billion)
    • Model Spec 2 (pytorch, 30 Billion)
    • Model Spec 3 (fp8, 480 Billion)
    • Model Spec 4 (fp8, 30 Billion)
    • Model Spec 5 (gptq, 480 Billion)
    • Model Spec 6 (gptq, 30 Billion)
    • Model Spec 7 (awq, 480 Billion)
    • Model Spec 8 (awq, 30 Billion)
    • Model Spec 9 (mlx, 480 Billion)
    • Model Spec 10 (mlx, 30 Billion)
    • Model Spec 11 (ggufv2, 480 Billion)
    • Model Spec 12 (ggufv2, 30 Billion)
• Qwen3-Instruct
  • Specifications
    • Model Spec 1 (pytorch, 235 Billion)
    • Model Spec 2 (pytorch, 30 Billion)
    • Model Spec 3 (pytorch, 4 Billion)
    • Model Spec 4 (fp8, 235 Billion)
    • Model Spec 5 (fp8, 30 Billion)
    • Model Spec 6 (fp8, 4 Billion)
    • Model Spec 7 (gptq, 235 Billion)
    • Model Spec 8 (gptq, 30 Billion)
    • Model Spec 9 (gptq, 4 Billion)
    • Model Spec 10 (awq, 235 Billion)
    • Model Spec 11 (awq, 30 Billion)
    • Model Spec 12 (awq, 4 Billion)
    • Model Spec 13 (mlx, 235 Billion)
    • Model Spec 14 (mlx, 30 Billion)
    • Model Spec 15 (mlx, 4 Billion)
    • Model Spec 16 (ggufv2, 235 Billion)
    • Model Spec 17 (ggufv2, 30 Billion)
    • Model Spec 18 (ggufv2, 4 Billion)
• Qwen3-Next-Instruct
  • Specifications
    • Model Spec 1 (pytorch, 80 Billion)
    • Model Spec 2 (fp8, 80 Billion)
    • Model Spec 3 (awq, 80 Billion)
    • Model Spec 4 (mlx, 80 Billion)
• Qwen3-Next-Thinking
  • Specifications
    • Model Spec 1 (pytorch, 80 Billion)
    • Model Spec 2 (fp8, 80 Billion)
    • Model Spec 3 (awq, 80 Billion)
    • Model Spec 4 (mlx, 80 Billion)
• Qwen3-Omni-Instruct
  • Specifications
    • Model Spec 1 (pytorch, 30 Billion)
    • Model Spec 2 (awq, 30 Billion)
• Qwen3-Omni-Thinking
  • Specifications
    • Model Spec 1 (pytorch, 30 Billion)
    • Model Spec 2 (awq, 30 Billion)
• Qwen3-Thinking
  • Specifications
    • Model Spec 1 (pytorch, 235 Billion)
    • Model Spec 2 (pytorch, 30 Billion)
    • Model Spec 3 (pytorch, 4 Billion)
    • Model Spec 4 (fp8, 235 Billion)
    • Model Spec 5 (fp8, 30 Billion)
    • Model Spec 6 (fp8, 4 Billion)
    • Model Spec 7 (gptq, 235 Billion)
    • Model Spec 8 (gptq, 30 Billion)
    • Model Spec 9 (gptq, 4 Billion)
    • Model Spec 10 (awq, 235 Billion)
    • Model Spec 11 (awq, 30 Billion)
    • Model Spec 12 (awq, 4 Billion)
    • Model Spec 13 (mlx, 235 Billion)
    • Model Spec 14 (mlx, 30 Billion)
    • Model Spec 15 (mlx, 4 Billion)
    • Model Spec 16 (ggufv2, 235 Billion)
    • Model Spec 17 (ggufv2, 30 Billion)
    • Model Spec 18 (ggufv2, 4 Billion)
• Qwen3-VL-Instruct
  • Specifications
    • Model Spec 1 (pytorch, 235 Billion)
    • Model Spec 2 (fp8, 235 Billion)
    • Model Spec 3 (awq, 235 Billion)
    • Model Spec 4 (pytorch, 30 Billion)
    • Model Spec 5 (fp8, 30 Billion)
    • Model Spec 6 (awq, 30 Billion)
    • Model Spec 7 (pytorch, 32 Billion)
    • Model Spec 8 (fp8, 32 Billion)
    • Model Spec 9 (awq, 32 Billion)
    • Model Spec 10 (pytorch, 8 Billion)
    • Model Spec 11 (fp8, 8 Billion)
    • Model Spec 12 (awq, 8 Billion)
    • Model Spec 13 (pytorch, 4 Billion)
    • Model Spec 14 (fp8, 4 Billion)
    • Model Spec 15 (awq, 4 Billion)
    • Model Spec 16 (pytorch, 2 Billion)
    • Model Spec 17 (fp8, 2 Billion)
    • Model Spec 18 (mlx, 235 Billion)
    • Model Spec 19 (mlx, 30 Billion)
    • Model Spec 20 (mlx, 32 Billion)
    • Model Spec 21 (mlx, 8 Billion)
    • Model Spec 22 (mlx, 2 Billion)
    • Model Spec 23 (mlx, 2 Billion)
    • Model Spec 24 (mlx, 4 Billion)
    • Model Spec 25 (mlx, 4 Billion)
    • Model Spec 26 (mlx, 4 Billion)
    • Model Spec 27 (mlx, 4 Billion)
    • Model Spec 28 (mlx, 8 Billion)
    • Model Spec 29 (mlx, 8 Billion)
    • Model Spec 30 (mlx, 8 Billion)
    • Model Spec 31 (mlx, 30 Billion)
    • Model Spec 32 (mlx, 30 Billion)
    • Model Spec 33 (mlx, 32 Billion)
• Qwen3-VL-Thinking
  • Specifications
    • Model Spec 1 (pytorch, 235 Billion)
    • Model Spec 2 (fp8, 235 Billion)
    • Model Spec 3 (awq, 235 Billion)
    • Model Spec 4 (pytorch, 30 Billion)
    • Model Spec 5 (fp8, 30 Billion)
    • Model Spec 6 (awq, 30 Billion)
    • Model Spec 7 (mlx, 235 Billion)
    • Model Spec 8 (mlx, 30 Billion)
    • Model Spec 9 (mlx, 2 Billion)
    • Model Spec 10 (mlx, 2 Billion)
    • Model Spec 11 (mlx, 2 Billion)
    • Model Spec 12 (mlx, 4 Billion)
    • Model Spec 13 (mlx, 4 Billion)
    • Model Spec 14 (mlx, 4 Billion)
    • Model Spec 15 (mlx, 4 Billion)
    • Model Spec 16 (mlx, 8 Billion)
    • Model Spec 17 (mlx, 8 Billion)
    • Model Spec 18 (mlx, 8 Billion)
    • Model Spec 19 (mlx, 8 Billion)
    • Model Spec 20 (mlx, 8 Billion)
    • Model Spec 21 (mlx, 30 Billion)
    • Model Spec 22 (mlx, 30 Billion)
    • Model Spec 23 (mlx, 235 Billion)
• qwen3.5
  • Specifications
    • Model Spec 1 (pytorch, 397 Billion)
    • Model Spec 2 (fp8, 397 Billion)
    • Model Spec 3 (gptq, 397 Billion)
    • Model Spec 4 (awq, 397 Billion)
    • Model Spec 5 (ggufv2, 397 Billion)
    • Model Spec 6 (mlx, 397 Billion)
    • Model Spec 7 (pytorch, 122 Billion)
    • Model Spec 8 (fp8, 122 Billion)
    • Model Spec 9 (gptq, 122 Billion)
    • Model Spec 10 (awq, 122 Billion)
    • Model Spec 11 (ggufv2, 122 Billion)
    • Model Spec 12 (mlx, 122 Billion)
    • Model Spec 13 (pytorch, 35 Billion)
    • Model Spec 14 (fp8, 35 Billion)
    • Model Spec 15 (gptq, 35 Billion)
    • Model Spec 16 (awq, 35 Billion)
    • Model Spec 17 (ggufv2, 35 Billion)
    • Model Spec 18 (mlx, 35 Billion)
    • Model Spec 19 (pytorch, 27 Billion)
    • Model Spec 20 (fp8, 27 Billion)
    • Model Spec 21 (gptq, 27 Billion)
    • Model Spec 22 (awq, 27 Billion)
    • Model Spec 23 (ggufv2, 27 Billion)
    • Model Spec 24 (mlx, 27 Billion)
    • Model Spec 25 (pytorch, 9 Billion)
    • Model Spec 26 (awq, 9 Billion)
    • Model Spec 27 (ggufv2, 9 Billion)
    • Model Spec 28 (mlx, 9 Billion)
    • Model Spec 29 (pytorch, 4 Billion)
    • Model Spec 30 (awq, 4 Billion)
    • Model Spec 31 (ggufv2, 4 Billion)
    • Model Spec 32 (mlx, 4 Billion)
    • Model Spec 33 (pytorch, 2 Billion)
    • Model Spec 34 (awq, 2 Billion)
    • Model Spec 35 (ggufv2, 2 Billion)
    • Model Spec 36 (mlx, 2 Billion)
    • Model Spec 37 (pytorch, 0_8 Billion)
    • Model Spec 38 (ggufv2, 0_8 Billion)
    • Model Spec 39 (mlx, 0_8 Billion)
• qwen3.6
  • Specifications
    • Model Spec 1 (pytorch, 35 Billion)
    • Model Spec 2 (fp8, 35 Billion)
    • Model Spec 3 (awq, 35 Billion)
    • Model Spec 4 (ggufv2, 35 Billion)
    • Model Spec 5 (mlx, 35 Billion)
• qwenLong-l1
  • Specifications
    • Model Spec 1 (pytorch, 32 Billion)
    • Model Spec 2 (awq, 32 Billion)
• QwQ-32B
  • Specifications
    • Model Spec 1 (pytorch, 32 Billion)
    • Model Spec 2 (awq, 32 Billion)
    • Model Spec 3 (mlx, 32 Billion)
    • Model Spec 4 (ggufv2, 32 Billion)
• QwQ-32B-Preview
  • Specifications
    • Model Spec 1 (pytorch, 32 Billion)
    • Model Spec 2 (awq, 32 Billion)
    • Model Spec 3 (ggufv2, 32 Billion)
    • Model Spec 4 (mlx, 32 Billion)
    • Model Spec 5 (mlx, 32 Billion)
    • Model Spec 6 (mlx, 32 Billion)
• seallm_v2
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (ggufv2, 7 Billion)
• seallm_v2.5
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (ggufv2, 7 Billion)
• seallms-v3
  • Specifications
    • Model Spec 1 (pytorch, 1_5 Billion)
    • Model Spec 2 (pytorch, 7 Billion)
• seed-oss
  • Specifications
    • Model Spec 1 (pytorch, 36 Billion)
    • Model Spec 2 (gptq, 36 Billion)
    • Model Spec 3 (awq, 36 Billion)
    • Model Spec 4 (mlx, 36 Billion)
    • Model Spec 5 (ggufv2, 36 Billion)
• Skywork
  • Specifications
    • Model Spec 1 (pytorch, 13 Billion)
• Skywork-Math
  • Specifications
    • Model Spec 1 (pytorch, 13 Billion)
• skywork-or1
  • Specifications
    • Model Spec 1 (pytorch, 32 Billion)
    • Model Spec 2 (gptq, 32 Billion)
    • Model Spec 3 (pytorch, 7 Billion)
    • Model Spec 4 (gptq, 7 Billion)
• skywork-or1-preview
  • Specifications
    • Model Spec 1 (pytorch, 32 Billion)
    • Model Spec 2 (gptq, 32 Billion)
    • Model Spec 3 (pytorch, 7 Billion)
    • Model Spec 4 (ggufv2, 32 Billion)
    • Model Spec 5 (ggufv2, 7 Billion)
• telechat
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (gptq, 7 Billion)
    • Model Spec 3 (pytorch, 12 Billion)
    • Model Spec 4 (gptq, 12 Billion)
    • Model Spec 5 (pytorch, 52 Billion)
• tiny-llama
  • Specifications
    • Model Spec 1 (ggufv2, 1 Billion)
• wizardcoder-python-v1.0
  • Specifications
    • Model Spec 1 (pytorch, 13 Billion)
    • Model Spec 2 (pytorch, 34 Billion)
    • Model Spec 3 (ggufv2, 7 Billion)
    • Model Spec 4 (ggufv2, 13 Billion)
    • Model Spec 5 (ggufv2, 34 Billion)
• wizardmath-v1.0
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (pytorch, 70 Billion)
• XiYanSQL-QwenCoder-2504
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (pytorch, 32 Billion)
• xverse
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (pytorch, 13 Billion)
    • Model Spec 3 (pytorch, 65 Billion)
• xverse-chat
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (pytorch, 13 Billion)
• Yi
  • Specifications
    • Model Spec 1 (ggufv2, 34 Billion)
    • Model Spec 2 (pytorch, 6 Billion)
    • Model Spec 3 (pytorch, 9 Billion)
    • Model Spec 4 (pytorch, 34 Billion)
• Yi-1.5
  • Specifications
    • Model Spec 1 (pytorch, 6 Billion)
    • Model Spec 2 (pytorch, 9 Billion)
    • Model Spec 3 (pytorch, 34 Billion)
• Yi-1.5-chat
  • Specifications
    • Model Spec 1 (pytorch, 6 Billion)
    • Model Spec 2 (pytorch, 9 Billion)
    • Model Spec 3 (pytorch, 34 Billion)
    • Model Spec 4 (ggufv2, 6 Billion)
    • Model Spec 5 (ggufv2, 9 Billion)
    • Model Spec 6 (ggufv2, 34 Billion)
    • Model Spec 7 (gptq, 6 Billion)
    • Model Spec 8 (gptq, 9 Billion)
    • Model Spec 9 (gptq, 34 Billion)
    • Model Spec 10 (awq, 6 Billion)
    • Model Spec 11 (awq, 9 Billion)
    • Model Spec 12 (awq, 34 Billion)
    • Model Spec 13 (mlx, 6 Billion)
    • Model Spec 14 (mlx, 6 Billion)
    • Model Spec 15 (mlx, 9 Billion)
    • Model Spec 16 (mlx, 9 Billion)
    • Model Spec 17 (mlx, 34 Billion)
    • Model Spec 18 (mlx, 34 Billion)
• Yi-1.5-chat-16k
  • Specifications
    • Model Spec 1 (pytorch, 9 Billion)
    • Model Spec 2 (pytorch, 34 Billion)
    • Model Spec 3 (ggufv2, 9 Billion)
    • Model Spec 4 (ggufv2, 34 Billion)
• Yi-200k
  • Specifications
    • Model Spec 1 (pytorch, 6 Billion)
    • Model Spec 2 (pytorch, 34 Billion)
• Yi-chat
  • Specifications
    • Model Spec 1 (gptq, 34 Billion)
    • Model Spec 2 (pytorch, 6 Billion)
    • Model Spec 3 (pytorch, 34 Billion)
    • Model Spec 4 (ggufv2, 34 Billion)