metadata

base_model: mistralai/Mistral-Small-Instruct-2409
language:
  - en
  - fr
  - de
  - es
  - it
  - pt
  - zh
  - ja
  - ru
  - ko
library_name: transformers
license: other
license_name: mrl
license_link: https://mistral.ai/licenses/MRL-0.1.md
tags:
  - llama-cpp
  - gguf-my-repo
inference: false
extra_gated_description: >-
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Triangle104/Mistral-Small-Instruct-2409-Q6_K-GGUF

This model was converted to GGUF format from mistralai/Mistral-Small-Instruct-2409 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Model details:

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.

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

And ping the client:

curl --location 'http://: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

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

[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."

The Mistral AI Team

Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Alok Kothari, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Augustin Garreau, Austin Birky, Bam4d, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Carole Rambaud, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Diogo Costa, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gaspard Blanchet, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Hichem Sattouf, Ian Mack, Jean-Malo Delignon, Jessica Chudnovsky, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickaël Seznec, Nicolas Schuhl, Niklas Muhs, Olivier de Garrigues, Patrick von Platen, Paul Jacob, Pauline Buche, Pavan Kumar Reddy, Perry Savas, Pierre Stock, Romain Sauvestre, Sagar Vaze, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibault Schueller, Thibaut Lavril, Thomas Wang, Théophile Gervet, Timothée Lacroix, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/Mistral-Small-Instruct-2409-Q6_K-GGUF --hf-file mistral-small-instruct-2409-q6_k.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Mistral-Small-Instruct-2409-Q6_K-GGUF --hf-file mistral-small-instruct-2409-q6_k.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/Mistral-Small-Instruct-2409-Q6_K-GGUF --hf-file mistral-small-instruct-2409-q6_k.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo Triangle104/Mistral-Small-Instruct-2409-Q6_K-GGUF --hf-file mistral-small-instruct-2409-q6_k.gguf -c 2048