Triangle104/Mistral-Nemo-Instruct-2407-Q6_K-GGUF

This model was converted to GGUF format from mistralai/Mistral-Nemo-Instruct-2407 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:

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: 14,336
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% Usage

The model can be used with three different frameworks

mistral_inference: See here
transformers: See here
NeMo: See nvidia/Mistral-NeMo-12B-Instruct

Mistral Inference

Install

It is recommended to use mistralai/Mistral-Nemo-Instruct-2407 with mistral-inference. For HF transformers code snippets, please keep scrolling.

pip install mistral_inference

Download

from huggingface_hub import snapshot_download from pathlib import Path

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

snapshot_download(repo_id="mistralai/Mistral-Nemo-Instruct-2407", allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], 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/Nemo-Instruct --instruct --max_tokens 256 --temperature 0.35

E.g. Try out something like:

How expensive would it be to ask a window cleaner to clean all windows in Paris. Make a reasonable guess in US Dollar.

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}/tekken.json") model = Transformer.from_folder(mistral_models_path)

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=64, 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

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.decode(out_tokens[0])

print(result)

Transformers

NOTE: Until a new release has been made, you need to install transformers from source:

pip install git+https://github.com/huggingface/transformers.git

If you want to use Hugging Face transformers to generate text, you can do something like this.

from transformers import pipeline

messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] chatbot = pipeline("text-generation", model="mistralai/Mistral-Nemo-Instruct-2407",max_new_tokens=128) chatbot(messages)

Function calling with transformers

To use this example, you'll need transformers version 4.42.0 or higher. Please see the function calling guide in the transformers docs for more information.

from transformers import AutoModelForCausalLM, AutoTokenizer import torch

model_id = "mistralai/Mistral-Nemo-Instruct-2407" tokenizer = AutoTokenizer.from_pretrained(model_id)

def get_current_weather(location: str, format: str): """ Get the current weather

Args:
    location: The city and state, e.g. San Francisco, CA
    format: The temperature unit to use. Infer this from the users location. (choices: ["celsius", "fahrenheit"])
"""
pass

conversation = [{"role": "user", "content": "What's the weather like in Paris?"}] tools = [get_current_weather]

format and tokenize the tool use prompt

inputs = tokenizer.apply_chat_template( conversation, tools=tools, add_generation_prompt=True, return_dict=True, return_tensors="pt", )

model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")

inputs.to(model.device) outputs = model.generate(**inputs, max_new_tokens=1000) print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Note that, for reasons of space, this example does not show a complete cycle of calling a tool and adding the tool call and tool results to the chat history so that the model can use them in its next generation. For a full tool calling example, please see the function calling guide, and note that Mistral does use tool call IDs, so these must be included in your tool calls and tool results. They should be exactly 9 alphanumeric characters.

Unlike previous Mistral models, Mistral Nemo requires smaller temperatures. We recommend to use a temperature of 0.3.

Limitations

The Mistral Nemo Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. 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, 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-Nemo-Instruct-2407-Q6_K-GGUF --hf-file mistral-nemo-instruct-2407-q6_k.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Mistral-Nemo-Instruct-2407-Q6_K-GGUF --hf-file mistral-nemo-instruct-2407-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-Nemo-Instruct-2407-Q6_K-GGUF --hf-file mistral-nemo-instruct-2407-q6_k.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo Triangle104/Mistral-Nemo-Instruct-2407-Q6_K-GGUF --hf-file mistral-nemo-instruct-2407-q6_k.gguf -c 2048

Triangle104
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Mistral-Nemo-Instruct-2407-Q6_K-GGUF