MistralLite-AWQ

MistralLite-AWQ Model#

MistralLite-AWQ is a version of the MistralLite model that was quantized using the AWQ method developed by Lin et al. (2023). The MistralLite-AWQ models are approximately 70% smaller than those of MistralLite whilst maintaining comparable performance.

Please refer to the original MistralLite model card for details about the model preparation and training processes.

MistralLite-AWQ Variants#

Dependencies#

• autoawq==0.2.5 – AutoAWQ was used to quantize the MistralLite model.
• vllm==0.4.2 – vLLM was used to host models for benchmarking.

Evaluations#

Long Context#

The following benchmark results are shown as accuracy (%) values, unless stated otherwise.

Topic Retrieval#

See https://lmsys.org/blog/2023-06-29-longchat/

Line Retrieval#

See https://lmsys.org/blog/2023-06-29-longchat/#longeval-results

Pass Key Retrieval#

See https://github.com/epfml/landmark-attention/blob/main/llama/run_test.py#L101

QuALITY (Question Answering with Long Input Texts, Yes!)#

See https://nyu-mll.github.io/quality/

Usage#

Inference via vLLM HTTP Host#

Launch Host#

python -m vllm.entrypoints.openai.api_server \
    --model amazon/MistralLite-AWQ \
    --quantization awq

Query Host#

curl -X POST http://localhost:8000/v1/completions \
    -H "Content-Type: application/json" \
    -d '{ "model": "amazon/MistralLite-AWQ",
          "prompt": "<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>",
          "temperature": 0,
          "echo": false
    }'

Inference via vLLM Offline Inference#

from vllm import LLM, SamplingParams

prompts = [
   "<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>",
]
sampling_params = SamplingParams(temperature=0, max_tokens=100)

llm = LLM(model="amazon/MistralLite-AWQ")

outputs = llm.generate(prompts, sampling_params)

# Print the outputs.
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

License#

Apache 2.0

Limitations#

Before using the MistralLite-AWQ model, it is important to perform your own independent assessment, and take measures to ensure that your use would comply with your own specific quality control practices and standards, and that your use would comply with the local rules, laws, regulations, licenses and terms that apply to you, and your content.