.. _models_llm_codestral-v0.1: ======================================== codestral-v0.1 ======================================== - **Context Length:** 32768 - **Model Name:** codestral-v0.1 - **Languages:** en - **Abilities:** generate - **Description:** 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 Specifications ^^^^^^^^^^^^^^ Model Spec 1 (pytorch, 22 Billion) ++++++++++++++++++++++++++++++++++++++++ - **Model Format:** pytorch - **Model Size (in billions):** 22 - **Quantizations:** 4-bit, 8-bit, none - **Engines**: vLLM, Transformers (vLLM only available for quantization none) - **Model ID:** mistralai/Mistral-7B-Instruct-v0.2 - **Model Hubs**: `Hugging Face `__ Execute the following command to launch the model, remember to replace ``${quantization}`` with your chosen quantization method from the options listed above:: xinference launch --model-engine ${engine} --model-name codestral-v0.1 --size-in-billions 22 --model-format pytorch --quantization ${quantization} Model Spec 2 (ggufv2, 22 Billion) ++++++++++++++++++++++++++++++++++++++++ - **Model Format:** ggufv2 - **Model Size (in billions):** 22 - **Quantizations:** Q2_K, Q3_K_S, Q3_K_M, Q3_K_L, Q4_K_S, Q4_K_M, Q5_K_S, Q5_K_M, Q6_K, Q8_0 - **Engines**: llama.cpp - **Model ID:** bartowski/Codestral-22B-v0.1-GGUF - **Model Hubs**: `Hugging Face `__ Execute the following command to launch the model, remember to replace ``${quantization}`` with your chosen quantization method from the options listed above:: xinference launch --model-engine ${engine} --model-name codestral-v0.1 --size-in-billions 22 --model-format ggufv2 --quantization ${quantization} Model Spec 3 (mlx, 22 Billion) ++++++++++++++++++++++++++++++++++++++++ - **Model Format:** mlx - **Model Size (in billions):** 22 - **Quantizations:** 4-bit - **Engines**: MLX - **Model ID:** mlx-community/Codestral-22B-v0.1-4bit - **Model Hubs**: `Hugging Face `__ Execute the following command to launch the model, remember to replace ``${quantization}`` with your chosen quantization method from the options listed above:: xinference launch --model-engine ${engine} --model-name codestral-v0.1 --size-in-billions 22 --model-format mlx --quantization ${quantization} Model Spec 4 (mlx, 22 Billion) ++++++++++++++++++++++++++++++++++++++++ - **Model Format:** mlx - **Model Size (in billions):** 22 - **Quantizations:** 8-bit - **Engines**: MLX - **Model ID:** mlx-community/Codestral-22B-v0.1-8bit - **Model Hubs**: `Hugging Face `__ Execute the following command to launch the model, remember to replace ``${quantization}`` with your chosen quantization method from the options listed above:: xinference launch --model-engine ${engine} --model-name codestral-v0.1 --size-in-billions 22 --model-format mlx --quantization ${quantization}