Update README.md

README.md

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----
-model_name: "Qwen3-0.6B-en-law-qa"
-finetuned_by: "Ahsan Ahmed Khan (Ontario)"
-model_type: "Fine-tuned Causal Language Model for Legal Q&A"
-base_model: "Qwen/Qwen3-0.6B"
-language: "en"
-finetuning_method: "LoRA (Low-Rank Adaptation)"
-license: "apache-2.0"
-datasets:
-  - "haistudy/en_law_qa"
-tags:
-  - "legal"
-  - "question-answering"
-  - "law"
-  - "instruction-tuned"
----
-
-# Model Card for Qwen3-0.6B-en-law-qa
-
-## Model Details
-- **Developed by:** [Your Name/Organization]
-- **Base Model:** [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)
-- **Dataset:** [haistudy/en_law_qa](https://huggingface.co/datasets/haistudy/en_law_qa)
-- **Language:** English
-- **License:** Apache 2.0
-- **Fine-tuning Approach:** Parameter-Efficient Fine-Tuning (LoRA)
-
-## Model Description
-Fine-tuned version of Qwen3-0.6B optimized for legal question answering. Trained on 5,560 legal QA pairs covering:
-- Contract law
-- Intellectual property
-- Criminal law
-- Family law
-- Environmental law
-
-## Intended Uses
-✅ Legal research assistance  
-✅ Legal education  
-✅ Explaining legal concepts  
-❌ Actual legal advice  
-❌ Handling sensitive personal legal matters
-
-## Training Configuration
-training_parameters:
-  epochs: 73 (partial training)
-  batch_size: 16
-  gradient_accumulation_steps: 16
-  learning_rate: 2e-4
-  optimizer: "paged_adamw_8bit"
-
-quantization:
-  load_in_4bit: true
-  bnb_4bit_quant_type: "nf4"
-  bnb_4bit_compute_dtype: "bfloat16"
-
-lora_config:
-  r: 8
-  lora_alpha: 32
-  target_modules: 
-    - "q_proj"
-    - "k_proj"
-    - "v_proj"
-    - "o_proj"
-  lora_dropout: 0.05
-  bias: "none"
-
-Usage Example
-
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from peft import PeftModel
-
-model_name = "Qwen/Qwen3-0.6B"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-base_model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
-model = PeftModel.from_pretrained(base_model, "your-username/Qwen3-0.6B-en-law-qa")
-
-# Create prompt
-question = "What are the key elements of a valid contract?"
-messages = [
-    {"role": "user", "content": question}
-]
-text = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True
-)
-
-# Generate response
-inputs = tokenizer(text, return_tensors="pt").to(model.device)
-outputs = model.generate(**inputs, max_new_tokens=256)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-Training Data
-yaml
-dataset_stats:
-  samples: 5560
-  format: |
-    <|im_start|>user
-    {Question}<|im_end|>
-    <|im_start|>assistant
-    {Answer}<|im_end|>
-data_sources:
-  - Contract law
-  - Intellectual property
-  - Criminal law
-  - Family law
-  - Environmental law
-Limitations
-Limited to knowledge in training data (2023 cutoff)
-
-May generate plausible but incorrect information
-
-Not a substitute for professional legal advice
-
-English-only capability
-
-Environmental Impact
-Hardware: 1 × NVIDIA T4 GPU (Google Colab)
-CO2 Emissions: ≈0.8 kg (estimated during partial training)
-Calculated using Machine Learning Impact calculator
-
-Contact
+---
+model_name: "Qwen3-0.6B-en-law-qa"
+finetuned_by: "Ahsan Ahmed Khan (Ontario)"
+model_type: "Fine-tuned Causal Language Model for Legal Q&A"
+base_model: "Qwen/Qwen3-0.6B"
+language: "en"
+finetuning_method: "LoRA (Low-Rank Adaptation)"
+license: "apache-2.0"
+datasets:
+  - "haistudy/en_law_qa"
+tags:
+  - "legal"
+  - "question-answering"
+  - "law"
+  - "instruction-tuned"
+---
+
+# Model Card for Qwen3-0.6B-en-law-qa
+
+## Model Details
+- **Developed by:** Ontario (Ahsan Ahmed KHan)
+- **Base Model:** [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)
+- **Dataset:** [haistudy/en_law_qa](https://huggingface.co/datasets/haistudy/en_law_qa)
+- **Language:** English
+- **License:** Apache 2.0
+- **Fine-tuning Approach:** Parameter-Efficient Fine-Tuning (LoRA)
+
+## Model Description
+Fine-tuned version of Qwen3-0.6B optimized for legal question answering. Trained on 5,560 legal QA pairs covering:
+- Contract law
+- Intellectual property
+- Criminal law
+- Family law
+- Environmental law
+
+## Intended Uses
+✅ Legal research assistance  
+✅ Legal education  
+✅ Explaining legal concepts  
+❌ Actual legal advice  
+❌ Handling sensitive personal legal matters
+
+## Training Configuration
+training_parameters:
+  epochs: 73 (partial training)
+  batch_size: 16
+  gradient_accumulation_steps: 16
+  learning_rate: 2e-4
+  optimizer: "paged_adamw_8bit"
+
+quantization:
+  load_in_4bit: true
+  bnb_4bit_quant_type: "nf4"
+  bnb_4bit_compute_dtype: "bfloat16"
+
+lora_config:
+  r: 8
+  lora_alpha: 32
+  target_modules: 
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+  lora_dropout: 0.05
+  bias: "none"
+
+Usage Example
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+model_name = "Qwen/Qwen3-0.6B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+base_model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
+model = PeftModel.from_pretrained(base_model, "your-username/Qwen3-0.6B-en-law-qa")
+
+# Create prompt
+question = "What are the key elements of a valid contract?"
+messages = [
+    {"role": "user", "content": question}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+# Generate response
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+Training Data
+yaml
+dataset_stats:
+  samples: 5560
+  format: |
+    <|im_start|>user
+    {Question}<|im_end|>
+    <|im_start|>assistant
+    {Answer}<|im_end|>
+data_sources:
+  - Contract law
+  - Intellectual property
+  - Criminal law
+  - Family law
+  - Environmental law
+Limitations
+Limited to knowledge in training data (2023 cutoff)
+
+May generate plausible but incorrect information
+
+Not a substitute for professional legal advice
+
+English-only capability
+
+Environmental Impact
+Hardware: 1 × NVIDIA T4 GPU (Google Colab)
+CO2 Emissions: ≈0.8 kg (estimated during partial training)
+Calculated using Machine Learning Impact calculator
+
+Contact
 For questions or feedback: ahsanahmedkhan@proton.me