Add YAML metadata for model card

README.md

@@ -1,238 +1,51 @@
-# Model Card: amk-coder-v2
-
-## Model Details
-- **Developed by:** amkyawdev
-- **Model type:** Language Model (LLM) - Code Generation
-- **Language(s):** Myanmar (my), English (en)
-- **License:** Apache-2.0
-- **Base Model:** Qwen/Qwen2.5-Coder-1.5B
-- **Model Size:** 2B parameters
-- **Released:** 2025
-
-## 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 |
-| Weight Decay | 0.01 |
-| Warmup Ratio | 0.03 |
-| Hardware | Kaggle Cloud (Dual NVIDIA T4 GPUs) |
-
-## 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:
-```
-
-**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
-```
-
-### 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
-Write a Python class for a Bank Account with deposit and withdrawal methods<|im_end|>
-<|im_start|>assistant
-Thought & Code:
-```
-
-**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
-```
-
-### 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 ရေးပါ<|im_end|>
-<|im_start|>assistant
-Thought & Code:
-```
-
-**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
-
-### Using Transformers
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_name = "amkyawdev/amk-coder-v2"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
-
-# Myanmar prompt
-prompt = """<|im_start|>system
-You are an expert Myanmar AI coding agent with tool access.<|im_end|>
-<|im_start|>user
-Python ဖြင့် hello world print လုပ်ပါ<|im_end|>
-<|im_start|>assistant
-Thought & Code:"""
-
-inputs = tokenizer(prompt, return_tensors="pt")
-outputs = model.generate(
-    **inputs, 
-    max_new_tokens=256,
-    temperature=0.2,
-    do_sample=True
-)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+---
+pipeline_tag: text-generation
+license: apache-2.0
+tags:
+- code-generation
+- myanmar
+- burmese
+- qwen
+- conversational
+- transformers
+library_name: transformers
+inference:
+  parameters:
+    max_new_tokens: 512
+    temperature: 0.2
+---
+
+# 🤖 amk-coder-agent
+
+Myanmar Coding Agent using **amk-coder-v2** (fine-tuned from Qwen2.5-Coder-1.5B)
+
+## Features
+- 🇲🇲 Myanmar & English language support
+- 💻 Code generation, debugging, explanation
+- 🔍 Web search integration
+- 🌐 Streaming responses
+- 🎨 Beautiful chat UI
+
+## Quick Start
+
+### Backend
+```bash
+cd backend
+pip install -r requirements.txt
+export HF_TOKEN=hf_your_token
+uvicorn app.main:app --host 0.0.0.0 --port 8000
 ```
 
-### Using vLLM (Production)
+### CLI
 ```bash
-vllm serve "amkyawdev/amk-coder-v2"
+cd cli
+python bot.py
 ```
 
-## Uses
-
-### Direct Use
-- 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 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 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:** ~4 hours
-- **Carbon Footprint:** ~0.2 kg CO2e (estimated)
-
-## Citation
-```bibtex
-@misc{amk-coder-v2,
-  author = {amkyawdev},
-  title = {amk-coder-v2: Myanmar Coding Agent Model},
-  year = {2025},
-  publisher = {HuggingFace},
-  url = {https://huggingface.co/amkyawdev/amk-coder-v2}
-}
-```
+## API Endpoints
+- `GET /` - Health check
+- `POST /chat` - Streaming chat (SSE)
+- `GET /demo` - Demo HTML interface
 
-## 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)
+## License
+Apache-2.0