---
base_model: unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---

# Uploaded  model

- **Developed by:** EpistemeAI2
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Mistral-Nemo-Instruct-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)

# Model Card for EpistemeAI2's Fireball-Mistral-Nemo-Instruct-emo-PHD, fine tuned Mistral-Nemo-Instruct-2407

The EpistemeAI2's Fireball-Mistral-Nemo-Instruct-emo-PHD , fine tuned Mistral-Nemo-Instruct-2407 Large Language Model (LLM) is an instruct fine-tuned version of the [Mistral-Nemo-Base-2407](https://huggingface.co/mistralai/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](https://mistral.ai/news/mistral-nemo/).

## 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)**

## Training data

Fireball-Mistral-Nemo-Instruct-emo-PHD is fine tuned by **simulated-emotions and philsophy in deduction reasoning, math and science** dataset


### Mistral Inference

#### Install

```
pip install mistral_inference
```

#### Download

```py
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="EpistemeAI2/Fireball-Mistral-Nemo-Instruct-emo-PHD", allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], local_dir=mistral_models_path)
```



### 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 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="EpistemeAI2/Fireball-Mistral-Nemo-Instruct-emo-PHD",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](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling)
in the `transformers` docs for more information.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "EpistemeAI2/Fireball-Mistral-Nemo-Instruct-emo-PHD"
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](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling), 
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.

> [!TIP]
> Unlike previous Mistral models, Mistral Nemo requires smaller temperatures. We recommend to use a temperature of 0.3.