Update README.md

README.md

@@ -11,6 +11,65 @@ 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 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" # Replace with your actual 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