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README.md

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+---
+license: apache-2.0
+language: en
+tags:
+- openbmb/MiniCPM4-0.5B
+- coding
+- code-generation
+- fine-tuned
+- qlora
+- gguf
+- instruction
+- python
+datasets:
+- TokenBender/code_instructions_122k_alpaca_style
+model_type: openbmb/MiniCPM4-0.5B
+base_model: openbmb/MiniCPM4-0.5B
+---
+
+# MiniCPM4-0.5B-Coding-Finetuned-v1
+
+This model is a fine-tuned version of `openbmb/MiniCPM4-0.5B` specialized for Python code generation tasks. It's designed to understand programming-related instructions and provide accurate and efficient Python code solutions.
+
+## 💻 Model Description
+
+- **Base Model**: `openbmb/MiniCPM4-0.5B`
+- **Fine-tuning Method**: **QLoRA** (Quantized Low-Rank Adaptation)
+- **Dataset**: `TokenBender/code_instructions_122k_alpaca_style` - A large dataset of coding instructions and their corresponding solutions.
+- **Training**: Optimized for instruction-based code generation using 4-bit quantization for efficiency.
+
+## ⚠️ Important Considerations
+
+- **Verify All Code**: Generated code may contain errors or be suboptimal. Always test and review the code thoroughly before using it in production environments.
+- **Security**: The generated code has not been vetted for security vulnerabilities. Be cautious when using it in security-sensitive applications.
+- **Not a Replacement for Developers**: This model is a tool to assist developers, not replace them. Human oversight and expertise are crucial.
+
+## 🚀 Usage
+
+### With `transformers`
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import torch
+
+model_id = "rohitnagareddy/MiniCPM4-0.5B-Coding-Finetuned-v1"
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    trust_remote_code=True
+)
+
+# Create conversation for a Python code-generation task
+messages = [
+    {"role": "system", "content": "You are an expert coding assistant."},
+    {"role": "user", "content": "Write a Python function that takes a list of integers and returns the sum of all even numbers in the list."}
+]
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer
+)
+
+# Generate response
+outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+print(outputs[0]["generated_text"])
+```
+
+## 🔧 GGUF Versions
+
+This repository includes quantized GGUF versions for use with `llama.cpp` and compatible tools:
+
+- `MiniCPM4-0.5B-Coding-Finetuned-v1.fp16.gguf` - Full precision (largest, best quality)
+- `MiniCPM4-0.5B-Coding-Finetuned-v1.Q8_0.gguf` - 8-bit quantization (good balance)
+- `MiniCPM4-0.5B-Coding-Finetuned-v1.Q5_K_M.gguf` - 5-bit quantization (smaller, fast)
+- `MiniCPM4-0.5B-Coding-Finetuned-v1.Q4_K_M.gguf` - 4-bit quantization (smallest, fastest)
+
+### Example with llama.cpp
+
+```bash
+./main -m ./MiniCPM4-0.5B-Coding-Finetuned-v1.Q4_K_M.gguf -n 256 -p "<|im_start|>system\nYou are an expert coding assistant.<|im_end|>\n<|im_start|>user\nCreate a Python function to find the factorial of a number.<|im_end|>\n<|im_start|>assistant\n"
+```
+
+## 📊 Training Details
+
+- **Training Epochs**: 1
+- **QLoRA Rank (r)**: 16
+- **QLoRA Alpha**: 32
+- **Learning Rate**: 2e-4
+- **Optimizer**: Paged AdamW 32-bit
+- **Target Modules**: Auto-detected linear layers
+
+---
+*Model created by rohitnagareddy using an automated Colab script.*