Mistral-Small-Instruct-2409-imatrix-GGUF

Quantizations of https://huggingface.co/mistralai/Mistral-Small-Instruct-2409

Inference Clients/UIs

• llama.cpp
• KoboldCPP
• text-generation-webui
• ollama

---

From original readme

Mistral-Small-Instruct-2409 is an instruct fine-tuned version with the following characteristics:

• 22B parameters
• Vocabulary to 32768
• Supports function calling
• 32k sequence length

Usage Examples

vLLM (recommended)

We recommend using this model with the vLLM library to implement production-ready inference pipelines.

Installation

Make sure you install vLLM >= v0.6.1.post1:

pip install --upgrade vllm

Also make sure you have mistral_common >= 1.4.1 installed:

pip install --upgrade mistral_common

You can also make use of a ready-to-go docker image.

Offline

from vllm import LLM
from vllm.sampling_params import SamplingParams

model_name = "mistralai/Mistral-Small-Instruct-2409"

sampling_params = SamplingParams(max_tokens=8192)

# note that running Mistral-Small on a single GPU requires at least 44 GB of GPU RAM
# If you want to divide the GPU requirement over multiple devices, please add *e.g.* `tensor_parallel=2`
llm = LLM(model=model_name, tokenizer_mode="mistral", config_format="mistral", load_format="mistral")

prompt = "How often does the letter r occur in Mistral?"

messages = [
    {
        "role": "user",
        "content": prompt
    },
]

outputs = llm.chat(messages, sampling_params=sampling_params)

print(outputs[0].outputs[0].text)

Server

You can also use Mistral Small in a server/client setting.

1. Spin up a server:

vllm serve mistralai/Mistral-Small-Instruct-2409 --tokenizer_mode mistral --config_format mistral --load_format mistral

Note: Running Mistral-Small on a single GPU requires at least 44 GB of GPU RAM.

If you want to divide the GPU requirement over multiple devices, please add e.g. --tensor_parallel=2

1. And ping the client:

curl --location 'http://<your-node-url>:8000/v1/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer token' \
--data '{
    "model": "mistralai/Mistral-Small-Instruct-2409",
    "messages": [
      {
        "role": "user",
        "content": "How often does the letter r occur in Mistral?"
      }
    ]
}'

Mistral-inference

We recommend using mistral-inference to quickly try out / "vibe-check" the model.

Install

Make sure to have mistral_inference >= 1.4.1 installed.

pip install mistral_inference --upgrade

Download

from huggingface_hub import snapshot_download
from pathlib import Path

mistral_models_path = Path.home().joinpath('mistral_models', '22B-Instruct-Small')
mistral_models_path.mkdir(parents=True, exist_ok=True)

snapshot_download(repo_id="mistralai/Mistral-Small-Instruct-2409", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path)

Chat

After installing mistral_inference, a mistral-chat CLI command should be available in your environment. You can chat with the model using

mistral-chat $HOME/mistral_models/22B-Instruct-Small --instruct --max_tokens 256

Instruct following

from mistral_inference.transformer import Transformer
from mistral_inference.generate import generate

from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.protocol.instruct.messages import UserMessage
from mistral_common.protocol.instruct.request import ChatCompletionRequest


tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tokenizer.model.v3")
model = Transformer.from_folder(mistral_models_path)

completion_request = ChatCompletionRequest(messages=[UserMessage(content="How often does the letter r occur in Mistral?")])

tokens = tokenizer.encode_chat_completion(completion_request).tokens

out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])

print(result)

Function calling

from mistral_common.protocol.instruct.tool_calls import Function, Tool
from mistral_inference.transformer import Transformer
from mistral_inference.generate import generate

from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.protocol.instruct.messages import UserMessage
from mistral_common.protocol.instruct.request import ChatCompletionRequest


tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tokenizer.model.v3")
model = Transformer.from_folder(mistral_models_path)

completion_request = ChatCompletionRequest(
    tools=[
        Tool(
            function=Function(
                name="get_current_weather",
                description="Get the current weather",
                parameters={
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "The city and state, e.g. San Francisco, CA",
                        },
                        "format": {
                            "type": "string",
                            "enum": ["celsius", "fahrenheit"],
                            "description": "The temperature unit to use. Infer this from the users location.",
                        },
                    },
                    "required": ["location", "format"],
                },
            )
        )
    ],
    messages=[
        UserMessage(content="What's the weather like today in Paris?"),
        ],
)

tokens = tokenizer.encode_chat_completion(completion_request).tokens

out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])

print(result)

Usage in Hugging Face Transformers

You can also use Hugging Face transformers library to run inference using various chat templates, or fine-tune the model. Example for inference:

from transformers import LlamaTokenizerFast, MistralForCausalLM
import torch

device = "cuda"
tokenizer = LlamaTokenizerFast.from_pretrained('mistralai/Mistral-Small-Instruct-2409')
tokenizer.pad_token = tokenizer.eos_token

model = MistralForCausalLM.from_pretrained('mistralai/Mistral-Small-Instruct-2409', torch_dtype=torch.bfloat16)
model = model.to(device)

prompt = "How often does the letter r occur in Mistral?"

messages = [
    {"role": "user", "content": prompt},
 ]

model_input = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(device)
gen = model.generate(model_input, max_new_tokens=150)
dec = tokenizer.batch_decode(gen)
print(dec)

And you should obtain

<s>
  [INST]
  How often does the letter r occur in Mistral?
  [/INST]
  To determine how often the letter "r" occurs in the word "Mistral,"
  we can simply count the instances of "r" in the word.
  The word "Mistral" is broken down as follows:
    - M
    - i
    - s
    - t
    - r
    - a
    - l
  Counting the "r"s, we find that there is only one "r" in "Mistral."
  Therefore, the letter "r" occurs once in the word "Mistral."
</s>