llama-3-instruct#

Context Length: 8192
Model Name: llama-3-instruct
Languages: en
Abilities: chat
Description: 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..

Specifications#

Model Spec 1 (ggufv2, 8 Billion)#

Model Format: ggufv2
Model Size (in billions): 8
Quantizations: IQ3_M, Q4_K_M, Q5_K_M, Q6_K, Q8_0
Model ID: lmstudio-community/Meta-Llama-3-8B-Instruct-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-name llama-3-instruct --size-in-billions 8 --model-format ggufv2 --quantization ${quantization}

Model Spec 2 (pytorch, 8 Billion)#

Model Format: pytorch
Model Size (in billions): 8
Quantizations: 4-bit, 8-bit, none
Model ID: meta-llama/Meta-Llama-3-8B-Instruct
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-name llama-3-instruct --size-in-billions 8 --model-format pytorch --quantization ${quantization}

Model Spec 3 (ggufv2, 70 Billion)#

Model Format: ggufv2
Model Size (in billions): 70
Quantizations: IQ1_M, IQ2_XS, Q4_K_M
Model ID: lmstudio-community/Meta-Llama-3-70B-Instruct-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-name llama-3-instruct --size-in-billions 70 --model-format ggufv2 --quantization ${quantization}

Model Spec 4 (pytorch, 70 Billion)#

Model Format: pytorch
Model Size (in billions): 70
Quantizations: 4-bit, 8-bit, none
Model ID: meta-llama/Meta-Llama-3-70B-Instruct
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-name llama-3-instruct --size-in-billions 70 --model-format pytorch --quantization ${quantization}