Instructions to use IntervitensInc/Mistral-Nemo-Base-2407-chatml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IntervitensInc/Mistral-Nemo-Base-2407-chatml with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="IntervitensInc/Mistral-Nemo-Base-2407-chatml")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("IntervitensInc/Mistral-Nemo-Base-2407-chatml")
model = AutoModelForCausalLM.from_pretrained("IntervitensInc/Mistral-Nemo-Base-2407-chatml")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use IntervitensInc/Mistral-Nemo-Base-2407-chatml with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IntervitensInc/Mistral-Nemo-Base-2407-chatml"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IntervitensInc/Mistral-Nemo-Base-2407-chatml",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/IntervitensInc/Mistral-Nemo-Base-2407-chatml

SGLang

How to use IntervitensInc/Mistral-Nemo-Base-2407-chatml with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "IntervitensInc/Mistral-Nemo-Base-2407-chatml" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IntervitensInc/Mistral-Nemo-Base-2407-chatml",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "IntervitensInc/Mistral-Nemo-Base-2407-chatml" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IntervitensInc/Mistral-Nemo-Base-2407-chatml",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use IntervitensInc/Mistral-Nemo-Base-2407-chatml with Docker Model Runner:
```
docker model run hf.co/IntervitensInc/Mistral-Nemo-Base-2407-chatml
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Version with added chatml tokens for finetuning.

Model Card for Mistral-Nemo-Base-2407

The Mistral-Nemo-Base-2407 Large Language Model (LLM) is a pretrained generative text model of 12B parameters 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%

Usage

The model can be used with three different frameworks

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

Mistral Inference

Install

It is recommended to use mistralai/Mistral-Nemo-Base-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-v0.1')
mistral_models_path.mkdir(parents=True, exist_ok=True)

snapshot_download(repo_id="mistralai/Mistral-Nemo-Base-2407", allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], local_dir=mistral_models_path)

Demo

After installing mistral_inference, a mistral-demo CLI command should be available in your environment.

mistral-demo $HOME/mistral_models/Nemo-v0.1

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 AutoModelForCausalLM, AutoTokenizer

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

model = AutoModelForCausalLM.from_pretrained(model_id)
inputs = tokenizer("Hello my name is", return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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

Note

Mistral-Nemo-Base-2407 is a pretrained base model and therefore does not have any moderation mechanisms.

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

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Safetensors

Model size

12B params

Tensor type

BF16

Model tree for IntervitensInc/Mistral-Nemo-Base-2407-chatml

Finetunes

3 models

Merges

22 models

Quantizations

1 model

IntervitensInc
/

Mistral-Nemo-Base-2407-chatml

Model Card for Mistral-Nemo-Base-2407

Key features

Model Architecture

Metrics

Main Benchmarks

Multilingual Benchmarks (MMLU)

Usage

Mistral Inference

Install

Download

Demo

Transformers

Note

The Mistral AI Team

Model tree for IntervitensInc/Mistral-Nemo-Base-2407-chatml

Spaces using IntervitensInc/Mistral-Nemo-Base-2407-chatml 9