metadata

base_model: EpistemeAI/Fireball-Mistral-Nemo-Base-2407-sft-v2.2a
language:
  - en
license: apache-2.0
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - mistral
  - trl

Fireball-12B

This model is super fine-tune to provide better coding and better response(from first fine-tune) than Llama-3.1-8B and Google Gemma 2 9B. Further fine tuned with ORPO method with dataset

reciperesearch/dolphin-sft-v0.1-preference

Benchmark

Training Dataset

Supervised fine-tuning with dataset:

candenizkocak/code-alpaca-297k
yahma/alpaca-cleaned

Uploaded model

Developed by: EpistemeAI
License: apache-2.0
Finetuned from model : EpistemeAI/Fireball-Mistral-Nemo-Base-2407-sft-v2.2a

This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.

Model Card for Fireball-12B

The Heavy fine-tuned Mistral-Nemo-Base-2407 Large Language Model (LLM) is a pretrained generative text model of 12B parameters 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.

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: 14,436
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

NOTE: Until a new release has been made, you need to install transformers from source:
pip install mistral_inference
pip install mistral-demo
pip install accelerate #GPU A100/L4
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.

from transformers import AutoModelForCausalLM, AutoTokenizer
from accelerate import Accelerator #Use only GPU A100/L4

accelerator = Accelerator() #Use only GPU A100/L4

model_id = "EpistemeAI/Fireball-12B"
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))

Unlike previous Mistral models, Mistral Nemo requires smaller temperatures. We recommend to use a temperature of 0.3.

Note

EpistemeAI/Fireball-12B is a pretrained base model and therefore does not have any moderation mechanisms.

Citation for yahma/alpaca-cleaned dataset

@misc{alpaca,
  author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
  title = {Stanford Alpaca: An Instruction-following LLaMA model},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}},
}