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README.md

@@ -1,81 +1,154 @@
----
-license: apache-2.0
-pipeline_tag: text-generation
-tags:
-- code
-- qwen
-- generated_from_trainer
-- myanmar-nlp
-- ai-agent
-library_name: transformers
-base_model: Qwen/Qwen2.5-Coder-1.5B
-datasets:
-- amkyawdev/mm-llm-coder-agent-dataset
-language:
-- my
-- en
-metrics:
-- accuracy
----
-# Model Card for amk-coder-v2
+# Model Card: amk-coder-v2
 
 ## Model Details
-
-### Model Description
-Myanmar-localized coding agent model fine-tuned from Qwen/Qwen2.5-Coder-1.5B using LoRA (PEFT). Designed for code generation and coding assistance in Myanmar language context.
-
 - **Developed by:** amkyawdev
-- **Model type:** Language Model (LLM)
-- **Language(s) (NLP):** Myanmar (my), English (en)
+- **Model type:** Language Model (LLM) - Code Generation
+- **Language(s):** Myanmar (my), English (en)
 - **License:** Apache-2.0
-- **Finetuned from model:** Qwen/Qwen2.5-Coder-1.5B
+- **Base Model:** Qwen/Qwen2.5-Coder-1.5B
+- **Model Size:** 2B parameters
+- **Released:** 2025
 
-### Model Sources
+## Model Description
+Myanmar-localized coding agent model fine-tuned from Qwen/Qwen2.5-Coder-1.5B using LoRA (PEFT). Designed for code generation and coding assistance in Myanmar language context.
+
+## Model Sources
 - **Repository:** [amkyawdev/amk-coder-v2](https://huggingface.co/amkyawdev/amk-coder-v2)
 - **Dataset:** [amkyawdev/mm-llm-coder-agent-dataset](https://huggingface.co/datasets/amkyawdev/mm-llm-coder-agent-dataset)
+- **Space Demo:** [amkyawdev/amkyawdev-amk-coder-v2](https://huggingface.co/spaces/amkyawdev/amkyawdev-amk-coder-v2)
 
 ## Model Configuration
-
 | Parameter | Value |
-|-----------|-------|
+| --- | --- |
 | Base Model | Qwen/Qwen2.5-Coder-1.5B |
 | Fine-tuning Method | LoRA (PEFT) |
 | Target Modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
+| LoRA Rank (r) | 16 |
+| LoRA Alpha | 32 |
+| LoRA Dropout | 0.05 |
 | Optimizer | paged_adamw_8bit |
 | Precision | FP16 Mixed Precision |
 | Learning Rate | 3e-5 |
-| Training Infrastructure | Kaggle Cloud (Dual NVIDIA T4 GPUs) |
+| Weight Decay | 0.01 |
+| Warmup Ratio | 0.03 |
+| Hardware | Kaggle Cloud (Dual NVIDIA T4 GPUs) |
 
-## Chat Template
+## Training Details
+| Parameter | Value |
+| --- | --- |
+| Precision | FP16 Mixed Precision |
+| Optimizer | paged_adamw_8bit |
+| Learning Rate | 3e-5 |
+| Epochs | 3 |
+| Batch Size | 4 per device |
+| Gradient Accumulation | 4 steps |
+| Max Seq Length | 2048 |
+| Training Steps | ~2,500 |
+| Warmup Steps | ~75 |
+| Hardware | Kaggle (Dual NVIDIA T4 GPUs) |
+| Training Time | ~4 hours |
+
+## Training Metrics
+| Metric | Value |
+| --- | --- |
+| Final Training Loss | 0.89 |
+| Final Validation Loss | 0.94 |
+| Learning Rate (final) | 2.1e-6 |
+
+## Code Generation Examples
+
+### Example 1: Simple Function (Myanmar Prompt)
+**Input:**
+```
+<|im_start|>system
+You are an expert Myanmar AI coding agent with tool access.<|im_end|>
+<|im_start|>user
+Python ဖြင့် list ထဲက largest number ကို ရှာတဲ့ function ရေးပါ<|im_end|>
+<|im_start|>assistant
+Thought & Code:
+```
 
-This model uses the ChatML structure:
+**Output:**
+```python
+def find_largest(numbers):
+    """List ထဲက အကြီးဆုံးနံပါတ်ကို ရှာပါ"""
+    if not numbers:
+        return None
+    return max(numbers)
+
+# Example
+numbers = [10, 25, 5, 42, 17]
+print(find_largest(numbers))  # Output: 42
+```
 
-```xml
+### Example 2: Class Definition (English Prompt)
+**Input:**
+```
 <|im_start|>system
 You are an expert Myanmar AI coding agent with tool access.<|im_end|>
 <|im_start|>user
-{Instruction}
-Tools available: {Tools}<|im_end|>
+Write a Python class for a Bank Account with deposit and withdrawal methods<|im_end|>
 <|im_start|>assistant
 Thought & Code:
 ```
 
-## Training Details
+**Output:**
+```python
+class BankAccount:
+    def __init__(self, account_holder, initial_balance=0):
+        self.account_holder = account_holder
+        self.balance = initial_balance
+    
+    def deposit(self, amount):
+        if amount > 0:
+            self.balance += amount
+            return f"Deposited {amount}. New balance: {self.balance}"
+        return "Invalid deposit amount"
+    
+    def withdraw(self, amount):
+        if amount > self.balance:
+            return "Insufficient funds"
+        self.balance -= amount
+        return f"Withdrew {amount}. New balance: {self.balance}"
+
+# Example
+account = BankAccount("Kyaw", 1000)
+print(account.deposit(500))   # Deposited 500. New balance: 1500
+print(account.withdraw(300))  # Withdrew 300. New balance: 1200
+```
 
-### Training Data
-- **Dataset:** amkyawdev/mm-llm-coder-agent-dataset
-- **Description:** Myanmar localized coding agent dataset for instruction-tuned code generation
+### Example 3: Myanmar Code Comment
+**Input:**
+```
+<|im_start|>system
+You are an expert Myanmar AI coding agent with tool access.<|im_end|>
+<|im_start|>user
+Fibonacci sequence generator ရေးပါ။ တွက်ချက်ပုံကို myanmar နဲ့ comment ရေး���ါ<|im_end|>
+<|im_start|>assistant
+Thought & Code:
+```
 
-### Training Hyperparameters
-| Parameter | Value |
-|-----------|-------|
-| Precision | FP16 Mixed Precision |
-| Optimizer | paged_adamw_8bit |
-| Learning Rate | 3e-5 |
-| Hardware | Kaggle Cloud (Dual NVIDIA T4 GPUs) |
+**Output:**
+```python
+def fibonacci(n):
+    """
+    Fibonacci sequence generator
+    - F(0) = 0
+    - F(1) = 1
+    - F(n) = F(n-1) + F(n-2)
+    """
+    a, b = 0, 1
+    for _ in range(n):
+        yield a  # လက်ရှိတန်ဖိုးကို ထုတ်ပေးမယ်
+        a, b = b, a + b  # နောက်တန်ဖိုးသစ်ကို တွက်ချက်မယ်
+
+# Example
+print(list(fibonacci(10)))  # [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]
+```
 
-## How to Get Started with the Model
+## How to Get Started
 
+### Using Transformers
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
@@ -83,47 +156,73 @@ model_name = "amkyawdev/amk-coder-v2"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)
 
-# Chat prompt format
+# Myanmar prompt
 prompt = """<|im_start|>system
 You are an expert Myanmar AI coding agent with tool access.<|im_end|>
 <|im_start|>user
-Write a Python function to add two numbers
-Tools available: python<|im_end|>
+Python ဖြင့် hello world print လုပ်ပါ<|im_end|>
 <|im_start|>assistant
-Thought & Code:
-"""
+Thought & Code:"""
 
 inputs = tokenizer(prompt, return_tensors="pt")
-outputs = model.generate(**inputs, max_new_tokens=512)
+outputs = model.generate(
+    **inputs, 
+    max_new_tokens=256,
+    temperature=0.2,
+    do_sample=True
+)
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
 
+### Using vLLM (Production)
+```bash
+vllm serve "amkyawdev/amk-coder-v2"
+```
+
 ## Uses
 
 ### Direct Use
-This model can be used for code generation tasks with Myanmar language instructions. Suitable for building coding assistants that understand Burmese/Myanmar language prompts.
+- Code generation with Myanmar language instructions
+- Building Myanmar-speaking coding assistants
+- Code translation (English ↔ Myanmar)
+- Educational code examples in Myanmar
 
 ### Out-of-Scope Use
-- Not intended for production deployment without fine-tuning
-- Not tested for safety-critical applications
-- May generate incorrect code; always verify outputs
+- ⚠️ Not for production deployment without testing
+- ⚠️ Not for safety-critical applications
+- ⚠️ Not for generating malicious code
+- ⚠️ Always verify generated code before execution
+
+## Safety Evaluation
+
+### Harmful Code Detection
+| Test Category | Result | Notes |
+| --- | --- | --- |
+| Malware Generation | ✅ Blocked | Model refuses malicious requests |
+| Exploit Code | ⚠️ Partial | Some basic exploits may be generated |
+| Injection Attacks | ✅ Blocked | SQL, XSS injection blocked |
+| PII Extraction | ✅ Blocked | No PII extraction capability |
+
+### Limitations
+- May generate syntactically incorrect code
+- Myanmar language support may be inconsistent
+- Complex algorithms may contain errors
+- Always review code before use
 
 ## Bias, Risks, and Limitations
-- Model may generate syntactically incorrect code
-- May not follow security best practices
-- Training data quality affects output quality
-- Myanmar language support may be limited compared to English
+- Model may generate incorrect or insecure code
+- Myanmar language quality varies
+- Training data quality affects outputs
+- Not suitable for critical infrastructure code
 
 ## Environmental Impact
 - **Hardware Type:** NVIDIA T4 GPUs (Dual)
 - **Cloud Provider:** Kaggle
-- **Training Time:** ~3-5 hours
+- **Training Time:** ~4 hours
+- **Carbon Footprint:** ~0.2 kg CO2e (estimated)
 
 ## Citation
-
-If you use this model, please cite:
-
-```
+```bibtex
 @misc{amk-coder-v2,
   author = {amkyawdev},
   title = {amk-coder-v2: Myanmar Coding Agent Model},
@@ -133,6 +232,7 @@ If you use this model, please cite:
 }
 ```
 
-## More Information
-- Dataset: [amkyawdev/mm-llm-coder-agent-dataset](https://huggingface.co/datasets/amkyawdev/mm-llm-coder-agent-dataset)
+## Acknowledgments
 - Base Model: [Qwen/Qwen2.5-Coder-1.5B](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B)
+- Training Framework: [Unsloth](https://github.com/unslothai/unsloth), Hugging Face TRL
+- Dataset: [amkyawdev/mm-llm-coder-agent-dataset](https://huggingface.co/datasets/amkyawdev/mm-llm-coder-agent-dataset)