en dataset:Arthur-LAGACHERIE/EntierInstruct-66k base_model:meta-llama/Llama-3.2-1B-Instruct base_model:finetune:meta-llama/Llama-3.2-1B-Instruct license:llama3.2 region:us
---
base_model:
- meta-llama/Llama-3.2-1B-Instruct
datasets:
- Arthur-LAGACHERIE/EntierInstruct-66k
language:
- en
license: llama3.2
---

A 1 billion parameters model, fine-tuned from Llama-3.2-1B-Instruct

# Model Card: Precis-1B-Instruct

## Model Details

**Model Name:** Precis-1B-Instruct  
**Base Model:** LLaMA 3.2 1B Instruct  
**Parameters:** 1.24 billion  

## Model Description

Precis-1B-Instruct is a fine-tuned language model designed to perform a wide variety of instruction-following tasks. It is based on the LLaMA 3.2 architecture with 1 billion parameters, fine-tuned using the `Arthur-LAGACHERIE/EntierInstruct-66k` dataset.

This model is intended to provide concise and accurate responses in natural language tasks, making it suitable for applications such as summarization, question answering, math, and dialogue systems.

---
## Training Details

**Dataset:**  
The `Arthur-LAGACHERIE/EntierInstruct-66k` dataset comprises a wide range of instruction-based prompts and outputs, including math, instruction following, and code. 

**Fine-Tuning Process:**  
The model was fine-tuned using the Hugging Face `peft` library with LoRA techniques (SFT).  

## Limitations and Risks

- **Limitations:**  
The model may not generalize well to tasks or inputs outside its training distribution. It is designed for English tasks and may perform poorly in other languages.  

- **Ethical Considerations:**  
Ensure the model is not used to generate harmful, misleading, or biased outputs. Users must comply with ethical AI guidelines.  



**Example Code:**  
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Arthur-LAGACHERIE/Precis-1B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
input_text = "What the answer to life."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
