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