API.md

mistralrs API

These are API docs for the mistralrs package.

Table of contents

• Full API docs: here
• Docs for the Which enum: here
• Example: here

Which

Each *_model_id may be a HF hub repo or a local path.

Additionally, for models without quantization, the model architecture should be provided as the arch parameter in contrast to GGUF models which encode the architecture in the file. It should be one of the following:

• mistral
• gemma
• mixtral
• llama
• phi2
• phi3
• qwen2

class Which(Enum):
    class Plain:
        model_id: str
        arch: Architecture
        tokenizer_json: str | None = None
        repeat_last_n: int = 64
    class XLora:
        arch: Architecture
        xlora_model_id: str
        order: str
        tgt_non_granular_index: int | None = None
        model_id: str | None = None
        tokenizer_json: str | None = None
        repeat_last_n: int = 64
    class Lora:
        arch: Architecture
        adapters_model_id: str
        order: str
        model_id: str | None = None
        tokenizer_json: str | None = None
        repeat_last_n: int = 64
    class GGUF:
        tok_model_id: str
        quantized_model_id: str
        quantized_filename: str
        tokenizer_json: str | None = None
        repeat_last_n: int = 64
    class XLoraGGUF:
        tok_model_id: str
        quantized_model_id: str
        quantized_filename: str
        xlora_model_id: str
        order: str
        tgt_non_granular_index: int | None = None
        tokenizer_json: str | None = None
        repeat_last_n: int = 64
    class LoraGGUF:
        tok_model_id: str
        quantized_model_id: str
        quantized_filename: str
        adapters_model_id: str
        order: str
        tokenizer_json: str | None = None
        repeat_last_n: int = 64
    class GGML:
        tok_model_id: str
        quantized_model_id: str
        quantized_filename: str
        tokenizer_json: str | None = None
        repeat_last_n: int = 64
    class XLoraGGML:
        tok_model_id: str
        quantized_model_id: str
        quantized_filename: str
        xlora_model_id: str
        order: str
        tgt_non_granular_index: int | None = None
        tokenizer_json: str | None = None
        repeat_last_n: int = 64
    class LoraGGML:
        tok_model_id: str
        quantized_model_id: str
        quantized_filename: str
        adapters_model_id: str
        order: str
        tokenizer_json: str | None = None
        repeat_last_n: int = 64

Example

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",
        tokenizer_json=None,
        repeat_last_n=64,
    )
)

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)