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 and Huggingface's TRL library.
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 and Huggingface's TRL library.
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.
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
NOTE: Until a new release has been made, you need to install transformers from source:
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
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))
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.