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---
base_model: unsloth/Mistral-Nemo-Base-2407-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---
# Fireball-Mistral-Nemo-12B-Philos
Supervised Fined tuned by dataset of philosophy, math, coding and languages.
# Original Model Card
# Model Card for Mistral-Nemo-Instruct-2407
The 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)**
### Mistral Inference
#### Install
It is recommended to use `mistralai/Mistral-Nemo-Base-2407` with [mistral-inference](https://github.com/mistralai/mistral-inference).
For HF transformers code snippets, please keep scrolling.
```
pip install mistral_inference
```
### 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 = "EpistemeAI2/Fireball-Mistral-Nemo-12B-Philos"
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.
# 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)
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