masabhuq/stl_phone_summarizer

STL Phone Summarizer

A conversational LLM for summarizing phone specifications into concise, appealing descriptions for e-commerce.
Model: LoRA fine-tuned Llama-3.2
Repo: masabhuq/stl_phone_summarizer

Installation

pip install unsloth torch

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Usage

1. Load Model and Tokenizer

from unsloth import FastLanguageModel
from unsloth.chat_templates import get_chat_template

model, tokenizer = FastLanguageModel.from_pretrained(
    "masabhuq/stl_phone_summarizer",
    max_seq_length=2048,
    dtype=None,          # Auto-detect (bfloat16 if supported)
    load_in_4bit=True,   # 4-bit quantization for memory efficiency
)
FastLanguageModel.for_inference(model)

2. Apply the Chat Template

tokenizer = get_chat_template(
    tokenizer,
    chat_template="llama-3.2",
    map_eos_token=True,
)

3. Prepare the Input

system_prompt = (
    "You are an expert at summarizing phone specifications into short, appealing key descriptions for an e-commerce site. "
    "Always output in exactly this format:\n"
    "Display: [concise display summary]\n"
    "Processor: [processor name]\n"
    "Camera: [camera highlights]\n"
    "Battery: [battery capacity and charging]\n"
    "Others: [comma-separated unique features]. "
    "Focus on desirable aspects like high refresh rates, zoom capabilities, fast charging, and unique features such as water resistance or special sensors. "
    "Do not include complicated keyboards that don't make sense on their own. "
    "Do not include words that are too technical to understand for someone who is not highly tech savvy. "
    "Output should be within 280 characters. Don't include anything like IPDC or IP64 or any such features in the result. Words starting with IP are not to be considered display feature."
)

specs = "Build: Glass front (Gorilla Glass 5), silicone polymer back (eco leather), plastic frame\nWeight: 178 g ..."

prompt = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": specs}
]

formatted_prompt = tokenizer.apply_chat_template(
    prompt,
    tokenize=False,
    add_generation_prompt=True,
)

4. Tokenize and Generate

import torch

inputs = tokenizer(formatted_prompt, return_tensors="pt").to("cuda")
outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
)

5. Post-process Output

generated_text = tokenizer.decode(outputs[0], skip_special_tokens=False)
# Extract the last paragraph and clean up
paragraphs = generated_text.strip().split("\n\n")
last_paragraph = paragraphs[-1]
clean_last_paragraph = last_paragraph.split("<|eot_id|>")[0].strip()
print(clean_last_paragraph)

6. Clean Up

Free GPU memory after inference: python model.cpu() torch.cuda.empty_cache()

Hardware Requirements

• GPU: CUDA-compatible GPU with ~4-6GB VRAM for 4-bit inference.
• CPU: Optional for offloading model after inference (model.cpu()).
• RAM: ~8GB system RAM for smooth operation with dataset processing.

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Notes

• Chat Template: The tokenizer is uploaded without a chat template. Always apply the template at runtime as shown above.
• System Prompt: Adjust the system prompt for your use case.
• Output Format: The model is trained to output in a strict format for easy parsing.
• Memory Management: Use model.cpu() and torch.cuda.empty_cache() to free GPU memory after inference, especially on low-VRAM GPUs.
• Inference Parameters: Adjust temperature and top_p for more or less creative outputs, and max_new_tokens for longer or shorter summaries.

---

Model Details

• Base Model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit
• Fine-Tuning: LoRA adapters with rank r=16, targeting modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"].
• Quantization: 4-bit for memory efficiency (~4-6GB VRAM).
• Training Data: A dataset of phone specifications (specs) paired with concise summaries (output) in the format shown above.
• Training Setup: Fine-tuned with trl.SFTTrainer, train_on_responses_only to focus on assistant responses, and Llama-3.2 chat template for single-turn interactions.
• Output Constraints: Summaries are limited to 280 characters, focusing on user-friendly features and avoiding technical terms like "IP68" or "IPDC".

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Dataset

The model was trained on a custom dataset (specs_list.json) containing pairs of detailed phone specifications and their corresponding summaries. Each entry includes: - specs: Detailed technical specs (e.g., display size, chipset, camera details). - output: A concise summary in the format: Display: [summary] Processor: [name] Camera: [highlights] Battery: [capacity and charging] Others: [features] The dataset emphasizes consumer-friendly features like high refresh rates, fast charging, and water resistance, avoiding overly technical terms.

License

This model is licensed under the Apache 2.0 License. See the LICENSE file in the repository for details.

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Citation

If you use this model, please cite the repository: bibtex @misc{stl_phone_summarizer, author = {masabhuq}, title = {STL Phone Summarizer: A Fine-Tuned Llama-3.2 Model for Phone Specification Summaries}, year = {2025}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/masabhuq/stl_phone_summarizer}} } ### 6. Clean Up Free GPU memory after inference: python model.cpu() torch.cuda.empty_cache()