Mistral Inference Release Notes

Pixtral

Mistral models can now 👀 !

pip install --upgrade mistral_inference

>= 1.4.0

Download:

from huggingface_hub import snapshot_download
from pathlib import Path
mistral_models_path = Path.home().joinpath('mistral_models', 'Pixtral')
mistral_models_path.mkdir(parents=True, exist_ok=True)
snapshot_download(repo_id = "mistralai/Pixtral-12B-2409", allow_patterns = ["params.json", "consolidated.safetensors", "tekken.json"], local_dir = mistral_models_path)

CLI example:

mistral-chat $HOME/mistral_models/Pixtral --instruct --max_tokens 256

E.g.

Try out something like:

Text prompt: What can you see on the following picture?

[You can input zero, one or more images now.]

Image path or url [Leave empty and press enter to finish image input]: https://picsum.photos/id/237/200/300

Image path or url [Leave empty and press enter to finish image input]:

I see a black dog lying on a wooden surface. The dog appears to be looking up, and its eyes are clearly visible.

Python:

1. Load the model

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, TextChunk, ImageURLChunk
from mistral_common.protocol.instruct.request import ChatCompletionRequest
tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tekken.json")
model = Transformer.from_folder(mistral_models_path)

2. Run:

url = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"
prompt = "Describe the image."
completion_request = ChatCompletionRequest(messages = [UserMessage(content = [ImageURLChunk(image_url = url), TextChunk(text = prompt)])])
encoded = tokenizer.encode_chat_completion(completion_request)
images = encoded.images
tokens = encoded.tokens
out_tokens, _ = generate([tokens], model, images = [images], max_tokens = 256, temperature = 0.35, eos_id = tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = 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}/tekken.json")
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 = 256, temperature = 0.35, eos_id = tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])
print(result)

Original source

Welcome Mistral-Nemo from Mistral 🤝 NVIDIA

Read more about Mistral-Nemo here.

Install

pip install mistral-inference>=1.3.0

Download

export NEMO_MODEL=$HOME/12B_NEMO_MODEL
wget https://models.mistralcdn.com/mistral-nemo-2407/mistral-nemo-instruct-2407.tar
mkdir -p $NEMO_MODEL
tar -xf mistral-nemo-instruct-v0.1.tar -C $NEMO_MODEL

Chat

mistral-chat $HOME/NEMO_MODEL --instruct --max_tokens 1024

or directly in Python:

import os
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_model("mistral-nemo")
model = Transformer.from_folder(os.environ.get("NEMO_MODEL"))
prompt = "How expensive would it be to ask a window cleaner to clean all windows in Paris. Make a reasonable guess in US Dollar."
completion_request = ChatCompletionRequest(messages = [UserMessage(content = prompt)])
tokens = tokenizer.encode_chat_completion(completion_request).tokens
out_tokens, _ = generate([tokens], model, max_tokens = 1024, temperature = 0.35, eos_id = tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.decode(out_tokens[0])
print(result)

Summary

The Mistral-Nemo-Instruct-2407 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-Nemo-Base-2407. Trained jointly by Mistral AI and NVIDIA, it significantly outperforms existing models smaller or similar in size.
For more details about this model please refer to our release blog post.

Key features

• Released under the Apache 2 License
• Pre-trained and instructed versions
• Trained with a 128k context window
• Trained on a large proportion of multilingual and code data
• Drop-in replacement of Mistral 7B

Model Architecture

Mistral Nemo is a transformer model, with the following architecture choices:

• Layers: 40
• Dim: 5,120
• Head dim: 128
• Hidden dim: 14,436
• Activation Function: SwiGLU
• Number of heads: 32
• Number of kv-heads: 8 (GQA)
• Vocabulary size: 2**17 ~= 128k
• Rotary embeddings (theta = 1M)

Metrics

Main Benchmarks

Benchmark Score HellaSwag (0-shot) 83.5% Winogrande (0-shot) 76.8% OpenBookQA (0-shot) 60.6% CommonSenseQA (0-shot) 70.4% TruthfulQA (0-shot) 50.3% MMLU (5-shot) 68.0% TriviaQA (5-shot) 73.8% NaturalQuestions (5-shot) 31.2%

Multilingual Benchmarks (MMLU)

Language Score French 62.3% German 62.7% Spanish 64.6% Italian 61.3% Portuguese 63.3% Russian 59.2% Chinese 59.0% Japanese 59.0%

What's Changed

• Tekken by @patrickvonplaten in #193

Original source

Welcome 🐍 Codestral-Mamba and 🔢 Mathstral

pip install mistral-inference>=1.2.0

Codestral-Mamba

pip install packaging mamba-ssm causal-conv1d transformers

1. Download

export MAMBA_CODE=$HOME/7B_MAMBA_CODE
wget https://models.mistralcdn.com/codestral-mamba-7b-v0-1/codestral-mamba-7B-v0.1.tar
mkdir -p $MAMBA_CODE
tar -xf codestral-mamba-7B-v0.1.tar -C $MAMBA_CODE

2. Chat

mistral-chat $HOME/7B_MAMBA_CODE --instruct --max_tokens 256

Mathstral

1. Download

export MATHSTRAL=$HOME/7B_MATH
wget https://models.mistralcdn.com/mathstral-7b-v0-1/mathstral-7B-v0.1.tar
mkdir -p $MATHSTRAL
tar -xf mathstral-7B-v0.1.tar -C $MATHSTRAL

2. Chat

mistral-chat $HOME/7B_MATH --instruct --max_tokens 256

Blogs:

• Blog Codestral Mamba 7B: https://mistral.ai/news/codestral-mamba/
• Blog Mathstral 7B: https://mistral.ai/news/mathstral/

What's Changed

• add a note about GPU requirement by @sophiamyang in #158
• Add codestral by @patrickvonplaten in #164
• Update README.md by @patrickvonplaten in #165
• fixing type in README.md by @didier-durand in #175
• Fix: typo in ModelArgs: "infered" to "inferred" by @CharlesCNorton in #174
• fix: typo in LoRALoaderMixin: correct "multipe" to "multiple" by @CharlesCNorton in #173
• fix: Correct typo in classifier.ipynb from "alborithm" to "algorithm" by @CharlesCNorton in #167
• Fix: typo in error message for state_dict validation by @CharlesCNorton in #172
• fix: Correct misspelling in ModelArgs docstring by @CharlesCNorton in #171
• Update README.md by @patrickvonplaten in #168
• fix: typo in HF_TOKEN environment variable check message by @CharlesCNorton in #179
• Adding Issue/Bug template. by @pandora-s-git in #178
• typo in ModelArgs class docstring. by @CharlesCNorton in #183
• Update README.md by @Simontwice in #184
• Add mamba by @patrickvonplaten in #187

New Contributors

• @didier-durand made their first contribution in #175
• @CharlesCNorton made their first contribution in #174
• @pandora-s-git made their first contribution in #178
• @Simontwice made their first contribution in #184

Full Changelog: v1.1.0...v1.2.0

Original source

mistral-inference==1.1.0 supports running LoRA models that were trained with: https://github.com/mistralai/mistral-finetune

Having trained a 7B base LoRA, you can run mistral-inference as follows:

from mistral_inference.model 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

MODEL_PATH = "path/to/downloaded/7B_base_dir"
tokenizer = MistralTokenizer.from_file(f"{MODEL_PATH}/tokenizer.model.v3")
# change to extracted tokenizer file
model = Transformer.from_folder(MODEL_PATH)
# change to extracted model dir
model.load_lora("/path/to/run_lora_dir/checkpoints/checkpoint_000300/consolidated/lora.safetensors")
completion_request = ChatCompletionRequest(messages = [UserMessage(content = "Explain Machine Learning to me in a nutshell.")])
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)

Assets 2

5 people reacted

Original source

Mistral-inference is the official inference library for all Mistral models: 7B, 8x7B, 8x22B.

Install with:

pip install mistral-inference

Run with:

from mistral_inference.model import Transformer
from mistral_inference 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

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.decode(out_tokens[0])
print(result)

Assets 2

Original source