ai/mistral-nemo
Mistral fine-tuned via NVIDIA NeMo for smoother enterprise use
10K+
ai/mistral-nemo repository overview
Mistral Nemo Instruct 2407
Mistral-Nemo-Instruct-2407 is an instruct fine-tuned large language model developed by Mistral AI and NVIDIA, optimized for multilingual tasks and instruction-following capabilities. Compared to Mistral 7B, it is much better at following precise instructions, reasoning, handling multi-turn conversations, and generating code.
Intended Uses
Mistral-Nemo-Instruct-2407 is designed for instruction-following tasks and multilingual applications, including:
- Conversational AI: Developing interactive, multilingual chatbots.
- Knowledge retrieval: Answering questions across multiple languages.
- Code assistance: Generating and understanding code snippets.
Characteristics
| Attribute | Details |
|---|---|
| Provider | Mistral AI & NVIDIA |
| Architecture | llama |
| Cutoff date | July 2024 |
| Languages | English, French, German, Spanish, Italian, Portuguese, Russian, Chinese, Japanese |
| Tool calling | ✅ |
| Input modalities | Text |
| Output modalities | Text |
| License | Apache 2.0 |
Available model variants
| Model variant | Parameters | Quantization | Context window | VRAM¹ | Size |
|---|---|---|---|---|---|
ai/mistral-nemo:latestai/mistral-nemo:12B-Q4_K_M | 12B | IQ2_XXS/Q4_K_M | 131K tokens | 7.78 GiB | 6.96 GB |
ai/mistral-nemo:12B-Q4_K_M | 12B | IQ2_XXS/Q4_K_M | 131K tokens | 7.78 GiB | 6.96 GB |
¹: VRAM estimated based on model characteristics.
latest→12B-Q4_K_M
Use this AI model with Docker Model Runner
First, pull the model:
docker model pull ai/mistral-nemo
Then run the model:
docker model run ai/mistral-nemo
For more information on Docker Model Runner, explore the documentation.
Benchmark performance
| 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% |
Links
Tag summary
Content type
Model
Digest
sha256:ccdfa597c…
Size
7 GB
Last updated
about 1 year ago
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