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---
base_model: unsloth/tinyllama-chat-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Fine-tuned TinyLlama for JSON Extraction
This repository contains a fine-tuned version of the `unsloth/tinyllama-chat-bnb-4bit` model, specifically trained for extracting product information from HTML snippets and outputting it in a JSON format.
## Model Details
- **Base Model:** `unsloth/tinyllama-chat-bnb-4bit`
- **Fine-tuning Method:** LoRA (Low-Rank Adaptation)
- **Trained on:** A custom dataset `json_extraction_dataset_500.json` of HTML product snippets and their corresponding JSON representations.
## Usage
This model can be used for tasks involving structured data extraction from HTML content.
### Loading the model
You can load the model and tokenizer using the `transformers` library:
```python
from unsloth import FastLanguageModel
import torch
import json
model_name = "learn-abc/html-model-tinyllama-chat-bnb-4bit" # Hugging face model repo ID
max_seq_length = 2048 # Or your chosen sequence length
dtype = None # Auto detection
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = model_name,
max_seq_length = max_seq_length,
dtype = dtype,
load_in_4bit = True,
)
FastLanguageModel.for_inference(model)
messages = [
{"role": "user", "content": "Extract the product information:\n<div class='product'><h2>iPad Air</h2><span class='price'>$1344</span><span class='category'>audio</span><span class='brand'>Dell</span></div>"}
]
inputs = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
).to("cuda") # Or "cpu" if not using GPU
outputs = model.generate(
input_ids=inputs,
max_new_tokens=256,
use_cache=True,
temperature=0.7,
do_sample=True,
top_p=0.9,
)
response = tokenizer.batch_decode(outputs)[0]
print(response)
```
# Uploaded model
- **Developed by:** learn-abc
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-chat-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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