File size: 2,060 Bytes
8b1a285 2ae0917 77c244b 414d667 77c244b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 |
---
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:**
```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))
|