llama3.2-3b-tuned/README.md

---
language:
- en
- hi
- te
license: llama3.2
license_link: https://www.llama.com/llama3_2/license/
base_model: meta-llama/Llama-3.2-3B
tags:
- llama
- llama-3.2
- fine-tuned
- qylis
- text-generation
- instruction-following
- medical
- finance
- insurance
- biology
- claims
- chemistry
pipeline_tag: text-generation
model_name: qylis/llama3.2-3b-tuned
datasets:
- gbharti/finance-alpaca
- medalpaca/medical_meadow_wikidoc
metrics:
- bleu
library_name: transformers
---

<div align="center">

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# 🦙 Qylis / Llama-3.2-3B-Tuned

**A fine-tuned Llama 3.2 3B model by [Qylis](https://qylis.com)**

[![Model](https://img.shields.io/badge/Model-Llama%203.2%203B-blue?style=flat-square&logo=meta)](https://huggingface.co/meta-llama/Llama-3.2-3B)
[![Fine-tuned by Qylis](https://img.shields.io/badge/Fine--tuned%20by-Qylis-6C3CE1?style=flat-square)](https://qylis.com)
[![License](https://img.shields.io/badge/License-Llama%203.2-orange?style=flat-square)](https://www.llama.com/llama3_2/license/)
[![HuggingFace](https://img.shields.io/badge/🤗%20HuggingFace-qylis-yellow?style=flat-square)](https://huggingface.co/qylis)

</div>

---

## 📖 Model Overview

`qylis/llama3.2-3b-tuned` is a fine-tuned version of Meta's [Llama 3.2 3B](https://huggingface.co/meta-llama/Llama-3.2-3B), developed and maintained by **Qylis**. This model has been adapted for enhanced instruction-following and domain-specific performance, leveraging Qylis's proprietary fine-tuning pipeline.

| Property | Details |
|---|---|
| **Base Model** | meta-llama/Llama-3.2-3B |
| **Model Type** | Causal Language Model (CLM) |
| **Architecture** | LlamaForCausalLM |
| **Parameters** | ~3 Billion |
| **Fine-tuned by** | Qylis |
| **Language** | English |
| **License** | Llama 3.2 Community License |

---

## 🚀 Quick Start

### Installation

```bash
pip install transformers torch accelerate
```

### Inference

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "qylis/llama3.2-3b-tuned"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.7,
        top_p=0.9,
        do_sample=True,
    )

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

### Pipeline API

```python
from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="qylis/llama3.2-3b-tuned",
    torch_dtype="auto",
    device_map="auto"
)

result = pipe("Your prompt here", max_new_tokens=256)
print(result[0]["generated_text"])
```

---

## 🎯 Intended Use

This model is intended for:

- **Instruction following** — Responding to natural language instructions
- **Text generation** — Generating coherent and contextually relevant text
- **Domain-specific tasks** — Applications fine-tuned by Qylis for specific use cases
- **Research and development** — Experimentation with fine-tuned LLMs

### Out-of-Scope Use

- Generating harmful, abusive, or misleading content
- High-stakes decision making without human oversight
- Use in applications requiring absolute factual accuracy without verification

---

## 🏋️ Training Details

| Property | Details |
|---|---|
| **Base Model** | meta-llama/Llama-3.2-3B |
| **Fine-tuning Method** | Supervised Fine-Tuning (SFT) |
| **Fine-tuned by** | Qylis |
| **Framework** | HuggingFace Transformers / PEFT |

> 📝 Additional training details, dataset information, and hyperparameters will be updated as documentation is finalized.

---

## 📊 Evaluation

> Benchmark results and evaluation metrics will be published here. Stay tuned for updates from the Qylis team.

---

## ⚠️ Limitations & Bias

Like all large language models, this model may:

- **Hallucinate** — Generate plausible-sounding but factually incorrect information
- **Reflect training biases** — Exhibit biases present in the training data
- **Struggle with long contexts** — Performance may degrade with very long inputs
- **Lack real-time knowledge** — No access to information beyond the training cutoff

Always validate outputs in production settings, especially for critical applications.

---

## 📜 License

This model is based on **Meta's Llama 3.2** and is subject to the [Llama 3.2 Community License Agreement](https://www.llama.com/llama3_2/license/). By using this model, you agree to the terms of that license.

> ⚠️ **Naming Requirement:** Per the Llama 3.2 Community License, any fine-tuned model distributed publicly must include **"Llama"** at the beginning of its name (e.g., `Llama-Qylis-3.2-3B-Tuned`). Please ensure your model name on HuggingFace complies with this requirement.

---

## 🤝 About Qylis

<div align="center">

**Qylis** is building next-generation AI solutions, from fine-tuned language models to production-ready AI applications.

🌐 [qylis.com](https://qylis.com) &nbsp;|&nbsp; 🤗 [HuggingFace](https://huggingface.co/qylis) &nbsp;|&nbsp; 📧 [Contact Us](mailto:hello@qylis.com)

</div>

---

## 📬 Citation

If you use this model in your research or application, please cite:

```bibtex
@misc{qylis2024llama32tuned,
  title        = {Qylis Llama-3.2-3B-Tuned},
  author       = {Qylis},
  year         = {2024},
  publisher    = {HuggingFace},
  howpublished = {\url{https://huggingface.co/qylis/llama3.2-3b-tuned}}
}
```