|
--- |
|
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) |
|
|
