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amazon
/
MistralLite

Text Generation
Transformers
PyTorch
mistral
text-generation-inference
Model card Files Files and versions
xet
Community
25

Instructions to use amazon/MistralLite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use amazon/MistralLite with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="amazon/MistralLite")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("amazon/MistralLite")
    model = AutoModelForCausalLM.from_pretrained("amazon/MistralLite")
  • Inference
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use amazon/MistralLite with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "amazon/MistralLite"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "amazon/MistralLite",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/amazon/MistralLite
  • SGLang

    How to use amazon/MistralLite with SGLang:

    Install from pip and serve model
    # Install SGLang from pip:
    pip install sglang
    # Start the SGLang server:
    python3 -m sglang.launch_server \
        --model-path "amazon/MistralLite" \
        --host 0.0.0.0 \
        --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "amazon/MistralLite",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker images
    docker run --gpus all \
        --shm-size 32g \
        -p 30000:30000 \
        -v ~/.cache/huggingface:/root/.cache/huggingface \
        --env "HF_TOKEN=<secret>" \
        --ipc=host \
        lmsysorg/sglang:latest \
        python3 -m sglang.launch_server \
            --model-path "amazon/MistralLite" \
            --host 0.0.0.0 \
            --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "amazon/MistralLite",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use amazon/MistralLite with Docker Model Runner:

    docker model run hf.co/amazon/MistralLite
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

Update README.md

#26 opened 4 months ago by
ReactionControl

Update README.md

#25 opened 9 months ago by
yinsong1986

add AIBOM

1
#24 opened 11 months ago by
RiccardoDav

Adding `safetensors` variant of this model

#23 opened over 1 year ago by
SFconvertbot

Error during model loading: CUDA error: out of memory

1
#22 opened almost 2 years ago by
saulorafaelfc

Extracting embeddings of the model output

#21 opened about 2 years ago by
AswathMG

Adding `safetensors` variant of this model

1
#20 opened about 2 years ago by
SFconvertbot

MistralLite just parrots back the prompt

#19 opened about 2 years ago by
wwgd

How to finetune Mistral to achieve this model?

#18 opened over 2 years ago by
KurtGD1915

MistralLite is not running on Text Generation Inference

5
#17 opened over 2 years ago by
soumodeep-semut

Fine Tuning

1
#16 opened over 2 years ago by
drachs

Prompt template for multi-turn conversations?

1
#15 opened over 2 years ago by
apepkuss79

Output breaks above 16k context length.

9
#14 opened over 2 years ago by
krecceg

How to cite?

1
#9 opened over 2 years ago by
paralym
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