engine/examples/python/cookbook.ipynb

Mistral.rs Python API Cookbook

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# First, install Rust: https://rustup.rs/
%pip install mistralrs-cuda -v

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from mistralrs import Runner, Which, ChatCompletionRequest

runner = Runner(
    which=Which.GGUF(
        tok_model_id="mistralai/Mistral-7B-Instruct-v0.1",
        quantized_model_id="TheBloke/Mistral-7B-Instruct-v0.1-GGUF",
        quantized_filename="mistral-7b-instruct-v0.1.Q4_K_M.gguf",
    )
)

res = runner.send_chat_completion_request(
    ChatCompletionRequest(
        model="mistral",
        messages=[
            {"role": "user", "content": "Tell me a story about the Rust type system."}
        ],
        max_tokens=256,
        presence_penalty=1.0,
        top_p=0.1,
        temperature=0.1,
    )
)
print(res)

Lets walk through this code.

from mistralrs import Runner, Which, ChatCompletionRequest

This imports the requires classes for our example. The Runner is a class which handles loading and running the model, which are enumerated by the Which class.

runner = Runner(
    which=Which.GGUF(
        tok_model_id="mistralai/Mistral-7B-Instruct-v0.1",
        quantized_model_id="TheBloke/Mistral-7B-Instruct-v0.1-GGUF",
        quantized_filename="mistral-7b-instruct-v0.1.Q4_K_M.gguf",
        tokenizer_json=None,
        repeat_last_n=64,
    )
)

This tells the Runner to actually load the model. It will use a CUDA, Metal, or CPU device depending on what features you set during compilation: here.

res = runner.send_chat_completion_request(
    ChatCompletionRequest(
        model="mistral",
        messages=[{"role":"user", "content":"Tell me a story about the Rust type system."}],
        max_tokens=256,
        presence_penalty=1.0,
        top_p=0.1,
        temperature=0.1,
    )
)
print(res)

Now we actually send a request! We can specify the messages just like with an OpenAI API.

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from mistralrs import Runner, Which, ChatCompletionRequest, Architecture

runner = Runner(
    which=Which.Plain(
        model_id="mistralai/Mistral-7B-Instruct-v0.1",
        arch=Architecture.Mistral,
    )
)

res = runner.send_chat_completion_request(
    ChatCompletionRequest(
        model="mistral",
        messages=[
            {"role": "user", "content": "Tell me a story about the Rust type system."}
        ],
        max_tokens=256,
        presence_penalty=1.0,
        top_p=0.1,
        temperature=0.1,
    )
)
print(res)

Lets walk through this code too and see the difference between loading a Plain model and loading a GGUF model.

from mistralrs import Runner, Which, ChatCompletionRequest, Architecture

This imports the requires classes for our example. The Runner is a class which handles loading and running the model, which are enumerated by the Which class. Note that we also import the Architecture enum which controls the non-GGUF model's architecture.

runner = Runner(
    which=Which.Plain(
        model_id="mistralai/Mistral-7B-Instruct-v0.1",
        arch=Architecture.Mistral,
        tokenizer_json=None,
        repeat_last_n=64,
    )
)

This tells the Runner to actually load the model as a Mistral architecture. It will use a CUDA, Metal, or CPU device depending on what features you set during compilation: here.

Loading a Mistral + GGUF model

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from mistralrs import Runner, Which

runner = Runner(
    which=Which.GGUF(
        tok_model_id="mistralai/Mistral-7B-Instruct-v0.1",
        quantized_model_id="TheBloke/Mistral-7B-Instruct-v0.1-GGUF",
        quantized_filename="mistral-7b-instruct-v0.1.Q4_K_M.gguf",
    )
)

Loading a plain Mistral model

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from mistralrs import Runner, Which, Architecture

runner = Runner(
    which=Which.Plain(
        tok_model_id="mistralai/Mistral-7B-Instruct-v0.1",
        arch=Architecture.Mistral,
    )
)

Loading an X-LoRA Zephyr model

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from mistralrs import Runner, Which

runner = Runner(
    which=Which.XLoraGGUF(
        tok_model_id=None,  # Automatically determine from ordering file
        quantized_model_id="TheBloke/zephyr-7B-beta-GGUF",
        quantized_filename="zephyr-7b-beta.Q4_0.gguf",
        xlora_model_id="lamm-mit/x-lora",
        order="orderings/xlora-paper-ordering.json",
        tgt_non_granular_index=None,
    )
)

Running the Runner

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from mistralrs import ChatCompletionRequest

res = runner.send_chat_completion_request(
    ChatCompletionRequest(
        model="mistral",
        messages=[
            {"role": "user", "content": "Tell me a story about the Rust type system."}
        ],
        max_tokens=256,
        presence_penalty=1.0,
        top_p=0.1,
        temperature=0.1,
    )
)
print(res.choices[0].message.content)
print(res.usage)