qylis/llama3.2-3b-tuned

🦙 Qylis / Llama-3.2-3B-Tuned

A fine-tuned Llama 3.2 3B model by Qylis

---

📖 Model Overview

qylis/llama3.2-3b-tuned is a fine-tuned version of Meta's 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

pip install transformers torch accelerate

Inference

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

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. 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

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

🌐 qylis.com  |  🤗 HuggingFace  |  📧 Contact Us

---

📬 Citation

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

@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}}
}