---
base_model: unsloth/Mistral-Nemo-Base-2407-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
datasets:
- meta-math/MetaMathQA
---

# Uploaded  model

- **Developed by:** EpistemeAI
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Mistral-Nemo-Base-2407-bnb-4bit
- 
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

# Fireball-MathMistral-Nemo-Base-2407

This model is fine-tune to provide better math response than Mistral-Nemo-Base-2407

## Training Dataset 
Supervised fine-tuning with datasets with meta-math/MetaMathQA



This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

# Model Card for Mistral-Nemo-Base-2407

The Fireball-MathMistral-Nemo-Base-2407 Large Language Model (LLM) is a pretrained generative text model of 12B parameters, it significantly outperforms existing models smaller or similar in size.

For more details about this model please refer to our release [blog post](https://mistral.ai/news/mistral-nemo/).

## Key features
- Released under the **Apache 2 License**
- 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)**

#### Demo

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


### Transformers

> [!IMPORTANT]
> NOTE: Until a new release has been made, you need to install transformers from source:
> ```sh
> 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.

```py
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "EpistemeAI/Fireball-MathMistral-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))
```

> [!TIP]
> 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.