code-llama#

  • Context Length: 100000

  • Model Name: code-llama

  • Languages: en

  • Abilities: generate

  • Description: Code-Llama is an open-source LLM trained by fine-tuning LLaMA2 for generating and discussing code.

Specifications#

Model Spec 1 (pytorch, 7 Billion)#

  • Model Format: pytorch

  • Model Size (in billions): 7

  • Quantizations: 4-bit, 8-bit, none

  • Engines: vLLM, Transformers (vLLM only available for quantization none)

  • Model ID: TheBloke/CodeLlama-7B-fp16

  • Model Hubs: Hugging Face, ModelScope

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 code-llama --size-in-billions 7 --model-format pytorch --quantization ${quantization}

Model Spec 2 (pytorch, 13 Billion)#

  • Model Format: pytorch

  • Model Size (in billions): 13

  • Quantizations: 4-bit, 8-bit, none

  • Engines: vLLM, Transformers (vLLM only available for quantization none)

  • Model ID: TheBloke/CodeLlama-13B-fp16

  • Model Hubs: Hugging Face, ModelScope

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 code-llama --size-in-billions 13 --model-format pytorch --quantization ${quantization}

Model Spec 3 (pytorch, 34 Billion)#

  • Model Format: pytorch

  • Model Size (in billions): 34

  • Quantizations: 4-bit, 8-bit, none

  • Engines: vLLM, Transformers (vLLM only available for quantization none)

  • Model ID: TheBloke/CodeLlama-34B-fp16

  • Model Hubs: Hugging Face, ModelScope

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 code-llama --size-in-billions 34 --model-format pytorch --quantization ${quantization}

Model Spec 4 (ggufv2, 7 Billion)#

  • Model Format: ggufv2

  • Model Size (in billions): 7

  • Quantizations: Q2_K, Q3_K_L, Q3_K_M, Q3_K_S, Q4_0, Q4_K_M, Q4_K_S, Q5_0, Q5_K_M, Q5_K_S, Q6_K, Q8_0

  • Engines: llama.cpp

  • Model ID: TheBloke/CodeLlama-7B-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 code-llama --size-in-billions 7 --model-format ggufv2 --quantization ${quantization}

Model Spec 5 (ggufv2, 13 Billion)#

  • Model Format: ggufv2

  • Model Size (in billions): 13

  • Quantizations: Q2_K, Q3_K_L, Q3_K_M, Q3_K_S, Q4_0, Q4_K_M, Q4_K_S, Q5_0, Q5_K_M, Q5_K_S, Q6_K, Q8_0

  • Engines: llama.cpp

  • Model ID: TheBloke/CodeLlama-13B-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 code-llama --size-in-billions 13 --model-format ggufv2 --quantization ${quantization}

Model Spec 6 (ggufv2, 34 Billion)#

  • Model Format: ggufv2

  • Model Size (in billions): 34

  • Quantizations: Q2_K, Q3_K_L, Q3_K_M, Q3_K_S, Q4_0, Q4_K_M, Q4_K_S, Q5_0, Q5_K_M, Q5_K_S, Q6_K, Q8_0

  • Engines: llama.cpp

  • Model ID: TheBloke/CodeLlama-34B-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 code-llama --size-in-billions 34 --model-format ggufv2 --quantization ${quantization}