license: apache-2.0
language:
- en
- fr
- de
- es
- it
- pt
- ru
- zh
- ja
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% |
Installation with mistral_inference
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
Generate with 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))
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