大语言模型

以下是 Xinference 中内置的 LLM 列表:

MODEL NAME	ABILITIES	COTNEXT_LENGTH	DESCRIPTION
aquila2	generate	2048	Aquila2 series models are the base language models
aquila2-chat	chat	2048	Aquila2-chat series models are the chat models
aquila2-chat-16k	chat	16384	AquilaChat2-16k series models are the long-text chat models
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.
c4ai-command-r-v01	chat	131072	C4AI Command-R(+) is a research release of a 35 and 104 billion parameter highly performant generative model.
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
cogvlm2	chat, vision	8192	CogVLM2 have achieved good results in many lists compared to the previous generation of CogVLM open source models. Its excellent performance can compete with some non-open source models.
cogvlm2-video-llama3-chat	chat, vision	8192	CogVLM2-Video achieves state-of-the-art performance on multiple video question answering tasks.
csg-wukong-chat-v0.1	chat	32768	csg-wukong-1B is a 1 billion-parameter small language model(SLM) pretrained on 1T tokens.
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-v2	generate	128000	DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference.
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-vl-chat	chat, vision	4096	DeepSeek-VL possesses general multimodal understanding capabilities, capable of processing logical diagrams, web pages, formula recognition, scientific literature, natural images, and embodied intelligence in complex scenarios.
gemma-2-it	chat	8192	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-it	chat	8192	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.
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.
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.
internlm2-chat	chat	32768	The second generation of the InternLM model, InternLM2.
internlm2.5-chat	chat	32768	InternLM2.5 series of the InternLM model.
internlm2.5-chat-1m	chat	262144	InternLM2.5 series of the InternLM model supports 1M long-context
internvl-chat	chat, vision	32768	InternVL 1.5 is an open-source multimodal large language model (MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding.
internvl2	chat, vision	32768	InternVL 2 is an open-source multimodal large language model (MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding.
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 collection of multimodal large language models (LLMs) is a collection of pretrained and instruction-tuned image reasoning generative models in 11B and 90B sizes (text + images in / text out)…
llama-3.2-vision-instruct	chat, vision	131072	The Llama 3.2-Vision-instruct instruction-tuned models are optimized for visual recognition, image reasoning, captioning, and answering general questions about an image. The models outperform many of the available open source and closed multimodal models on common industry benchmarks…
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-llama3-v-2_5	chat, vision	8192	MiniCPM-Llama3-V 2.5 is the latest model in the MiniCPM-V series. The model is built on SigLip-400M and Llama3-8B-Instruct with a total of 8B parameters.
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.
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.
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.
omnilmm	chat, vision	2048	OmniLMM is a family of open-source large multimodal models (LMMs) adept at vision & language modeling.
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.
orion-chat-rag	chat	4096	Orion-14B series models are open-source multilingual large language models trained from scratch by OrionStarAI.
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.
platypus2-70b-instruct	generate	4096	Platypus-70B-instruct is a merge of garage-bAInd/Platypus2-70B and upstage/Llama-2-70b-instruct-v2.
qwen-chat	chat	32768	Qwen-chat is a fine-tuned version of the Qwen LLM trained with alignment techniques, specializing in chatting.
qwen-vl-chat	chat, vision	4096	Qwen-VL-Chat supports more flexible interaction, such as multiple image inputs, multi-round question answering, and creative capabilities.
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	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-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.
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
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.
starling-lm	chat	4096	We introduce Starling-7B, an open large language model (LLM) trained by Reinforcement Learning from AI Feedback (RLAIF). The model harnesses the power of our new GPT-4 labeled ranking dataset
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.
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.
yi-coder	generate	131072	Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters.Excelling in long-context understanding with a maximum context length of 128K tokens.Supporting 52 major programming languages, including popular ones such as Java, Python, JavaScript, and C++.
yi-coder-chat	chat	131072	Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters.Excelling in long-context understanding with a maximum context length of 128K tokens.Supporting 52 major programming languages, including popular ones such as Java, Python, JavaScript, and C++.
yi-vl-chat	chat, vision	4096	Yi Vision Language (Yi-VL) model is the open-source, multimodal version of the Yi Large Language Model (LLM) series, enabling content comprehension, recognition, and multi-round conversations about images.

• aquila2
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (pytorch, 34 Billion)
    • Model Spec 3 (pytorch, 70 Billion)
• aquila2-chat
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (ggufv2, 34 Billion)
    • Model Spec 3 (gptq, 34 Billion)
    • Model Spec 4 (awq, 34 Billion)
    • Model Spec 5 (pytorch, 34 Billion)
    • Model Spec 6 (pytorch, 70 Billion)
• aquila2-chat-16k
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (ggufv2, 34 Billion)
    • Model Spec 3 (gptq, 34 Billion)
    • Model Spec 4 (awq, 34 Billion)
    • Model Spec 5 (pytorch, 34 Billion)
• 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)
• c4ai-command-r-v01
  • Specifications
    • Model Spec 1 (pytorch, 35 Billion)
    • Model Spec 2 (pytorch, 35 Billion)
    • Model Spec 3 (ggufv2, 35 Billion)
    • Model Spec 4 (pytorch, 104 Billion)
    • Model Spec 5 (pytorch, 104 Billion)
    • Model Spec 6 (gptq, 104 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)
• cogvlm2
  • Specifications
    • Model Spec 1 (pytorch, 20 Billion)
    • Model Spec 2 (pytorch, 20 Billion)
• cogvlm2-video-llama3-chat
  • Specifications
    • Model Spec 1 (pytorch, 12 Billion)
• csg-wukong-chat-v0.1
  • Specifications
    • Model Spec 1 (pytorch, 1 Billion)
    • Model Spec 2 (ggufv2, 1 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-v2
  • Specifications
    • Model Spec 1 (pytorch, 16 Billion)
    • Model Spec 2 (pytorch, 236 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-vl-chat
  • Specifications
    • Model Spec 1 (pytorch, 1_3 Billion)
    • Model Spec 2 (pytorch, 7 Billion)
• gemma-2-it
  • Specifications
    • Model Spec 1 (pytorch, 2 Billion)
    • Model Spec 2 (pytorch, 9 Billion)
    • Model Spec 3 (pytorch, 27 Billion)
    • Model Spec 4 (ggufv2, 2 Billion)
    • Model Spec 5 (ggufv2, 9 Billion)
    • Model Spec 6 (ggufv2, 27 Billion)
    • Model Spec 7 (mlx, 2 Billion)
    • Model Spec 8 (mlx, 2 Billion)
    • Model Spec 9 (mlx, 2 Billion)
    • Model Spec 10 (mlx, 9 Billion)
    • Model Spec 11 (mlx, 9 Billion)
    • Model Spec 12 (mlx, 9 Billion)
    • Model Spec 13 (mlx, 27 Billion)
    • Model Spec 14 (mlx, 27 Billion)
    • Model Spec 15 (mlx, 27 Billion)
• gemma-it
  • Specifications
    • Model Spec 1 (pytorch, 2 Billion)
    • Model Spec 2 (pytorch, 7 Billion)
• glm-4v
  • Specifications
    • Model Spec 1 (pytorch, 9 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)
• internlm2-chat
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (pytorch, 20 Billion)
• internlm2.5-chat
  • Specifications
    • Model Spec 1 (pytorch, 1_8 Billion)
    • Model Spec 2 (pytorch, 7 Billion)
    • Model Spec 3 (pytorch, 20 Billion)
    • Model Spec 4 (gptq, 7 Billion)
    • Model Spec 5 (ggufv2, 1_8 Billion)
    • Model Spec 6 (ggufv2, 7 Billion)
    • Model Spec 7 (ggufv2, 20 Billion)
    • Model Spec 8 (mlx, 7 Billion)
    • Model Spec 9 (mlx, 7 Billion)
• internlm2.5-chat-1m
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (gptq, 7 Billion)
    • Model Spec 3 (ggufv2, 7 Billion)
• internvl-chat
  • Specifications
    • Model Spec 1 (pytorch, 2 Billion)
    • Model Spec 2 (pytorch, 4 Billion)
    • Model Spec 3 (pytorch, 26 Billion)
• internvl2
  • Specifications
    • Model Spec 1 (pytorch, 1 Billion)
    • Model Spec 2 (pytorch, 2 Billion)
    • Model Spec 3 (awq, 2 Billion)
    • Model Spec 4 (pytorch, 4 Billion)
    • Model Spec 5 (pytorch, 8 Billion)
    • Model Spec 6 (awq, 8 Billion)
    • Model Spec 7 (pytorch, 26 Billion)
    • Model Spec 8 (awq, 26 Billion)
    • Model Spec 9 (pytorch, 40 Billion)
    • Model Spec 10 (awq, 40 Billion)
    • Model Spec 11 (pytorch, 76 Billion)
    • Model Spec 12 (awq, 76 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 (ggufv2, 7 Billion)
    • Model Spec 2 (ggufv2, 13 Billion)
    • Model Spec 3 (ggufv2, 70 Billion)
    • Model Spec 4 (pytorch, 7 Billion)
    • Model Spec 5 (gptq, 7 Billion)
    • Model Spec 6 (gptq, 70 Billion)
    • Model Spec 7 (awq, 70 Billion)
    • Model Spec 8 (awq, 7 Billion)
    • Model Spec 9 (pytorch, 13 Billion)
    • Model Spec 10 (gptq, 13 Billion)
    • Model Spec 11 (awq, 13 Billion)
    • Model Spec 12 (pytorch, 70 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 (ggufv2, 8 Billion)
    • Model Spec 2 (pytorch, 8 Billion)
    • Model Spec 3 (ggufv2, 70 Billion)
    • Model Spec 4 (pytorch, 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 (ggufv2, 8 Billion)
    • Model Spec 2 (pytorch, 8 Billion)
    • Model Spec 3 (pytorch, 8 Billion)
    • Model Spec 4 (gptq, 8 Billion)
    • Model Spec 5 (awq, 8 Billion)
    • Model Spec 6 (ggufv2, 70 Billion)
    • Model Spec 7 (pytorch, 70 Billion)
    • Model Spec 8 (pytorch, 70 Billion)
    • Model Spec 9 (gptq, 70 Billion)
    • Model Spec 10 (awq, 70 Billion)
    • Model Spec 11 (mlx, 8 Billion)
    • Model Spec 12 (mlx, 8 Billion)
    • Model Spec 13 (mlx, 8 Billion)
    • Model Spec 14 (mlx, 70 Billion)
    • Model Spec 15 (mlx, 70 Billion)
    • Model Spec 16 (mlx, 70 Billion)
    • Model Spec 17 (pytorch, 405 Billion)
    • Model Spec 18 (gptq, 405 Billion)
    • Model Spec 19 (awq, 405 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-Llama3-V-2_5
  • Specifications
    • Model Spec 1 (pytorch, 8 Billion)
    • Model Spec 2 (pytorch, 8 Billion)
• MiniCPM-V-2.6
  • 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)
• 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)
• OmniLMM
  • Specifications
    • Model Spec 1 (pytorch, 3 Billion)
    • Model Spec 2 (pytorch, 12 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)
• orion-chat-rag
  • Specifications
    • Model Spec 1 (pytorch, 14 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)
• platypus2-70b-instruct
  • Specifications
    • Model Spec 1 (pytorch, 70 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)
• qwen-vl-chat
  • Specifications
    • Model Spec 1 (pytorch, 7 Billion)
    • Model Spec 2 (gptq, 7 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
  • Specifications
    • Model Spec 1 (pytorch, 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 (pytorch, 7 Billion)
    • Model Spec 6 (gptq, 7 Billion)
    • Model Spec 7 (gptq, 7 Billion)
    • Model Spec 8 (awq, 7 Billion)
    • Model Spec 9 (pytorch, 72 Billion)
    • Model Spec 10 (awq, 72 Billion)
    • Model Spec 11 (gptq, 72 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, 1_5 Billion)
    • Model Spec 2 (pytorch, 7 Billion)
• qwen2.5-coder-instruct
  • Specifications
    • Model Spec 1 (pytorch, 1_5 Billion)
    • Model Spec 2 (pytorch, 7 Billion)
    • Model Spec 3 (ggufv2, 1_5 Billion)
    • Model Spec 4 (ggufv2, 7 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 (ggufv2, 72 Billion)
    • Model Spec 29 (mlx, 0_5 Billion)
    • Model Spec 30 (mlx, 0_5 Billion)
    • Model Spec 31 (mlx, 0_5 Billion)
    • Model Spec 32 (mlx, 1_5 Billion)
    • Model Spec 33 (mlx, 1_5 Billion)
    • Model Spec 34 (mlx, 1_5 Billion)
    • Model Spec 35 (mlx, 3 Billion)
    • Model Spec 36 (mlx, 3 Billion)
    • Model Spec 37 (mlx, 3 Billion)
    • Model Spec 38 (mlx, 7 Billion)
    • Model Spec 39 (mlx, 7 Billion)
    • Model Spec 40 (mlx, 7 Billion)
    • Model Spec 41 (mlx, 14 Billion)
    • Model Spec 42 (mlx, 14 Billion)
    • Model Spec 43 (mlx, 14 Billion)
    • Model Spec 44 (mlx, 32 Billion)
    • Model Spec 45 (mlx, 32 Billion)
    • Model Spec 46 (mlx, 32 Billion)
    • Model Spec 47 (mlx, 72 Billion)
    • Model Spec 48 (mlx, 72 Billion)
    • Model Spec 49 (mlx, 72 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)
• Skywork
  • Specifications
    • Model Spec 1 (pytorch, 13 Billion)
• Skywork-Math
  • Specifications
    • Model Spec 1 (pytorch, 13 Billion)
• Starling-LM
  • Specifications
    • Model Spec 1 (pytorch, 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)
• 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)
• yi-coder
  • Specifications
    • Model Spec 1 (pytorch, 9 Billion)
    • Model Spec 2 (pytorch, 1_5 Billion)
• yi-coder-chat
  • Specifications
    • Model Spec 1 (pytorch, 9 Billion)
    • Model Spec 2 (pytorch, 1_5 Billion)
• yi-vl-chat
  • Specifications
    • Model Spec 1 (pytorch, 6 Billion)
    • Model Spec 2 (pytorch, 34 Billion)