---
license: apache-2.0
language:
- en
library_name: peft
tags:
- base_model:adapter:distilgpt2
- lora
- transformers
base_model: distilgpt2
pipeline_tag: text-generation
---

# NextStep-Coder-MoE

A high-performance tiny coding LLM with **Interleaved Thinking** capability for advanced reasoning and agentic workflows.

## Model Description

NextStep-Coder-MoE is a LoRA fine-tuned model based on Qwen2.5-Coder-1.5B, optimized for:
- Chain-of-Thought reasoning with `<think>...</think>` tags
- Multi-step coding tasks
- Agentic workflows (plan → act → reflect)

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load model
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-1.5B")
model = PeftModel.from_pretrained(base_model, "OsamaBinLikhon/NextStep-Coder-MoE")
tokenizer = AutoTokenizer.from_pretrained("OsamaBinLikhon/NextStep-Coder-MoE")

# Generate
prompt = "Write a Python function to check if a number is prime.\n<think>"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0]))
```

## Training Details

| Parameter | Value |
|-----------|-------|
| Base Model | Qwen/Qwen2.5-Coder-1.5B |
| Method | LoRA (r=16, alpha=32) |
| Trainable Params | 18.4M (1.18%) |
| Precision | bf16 |
| Framework | Transformers + PEFT |

## Interleaved Thinking Format

The model uses `<think>` tags to show reasoning:

```
User: Write a binary search function.

Model: <think>
I need to implement binary search on a sorted array.
Key steps: find middle, compare, narrow search space.
Edge case: empty array returns -1.
</think>

def binary_search(arr, target):
    left, right = 0, len(arr) - 1
    while left <= right:
        mid = (left + right) // 2
        if arr[mid] == target:
            return mid
        elif arr[mid] < target:
            left = mid + 1
        else:
            right = mid - 1
    return -1
```

## Intended Use

- Code generation with step-by-step reasoning
- Debugging and code review
- Algorithm design and explanation
- Educational coding assistance

## Limitations

- Best for Python, may vary for other languages
- Requires `<think>` tag retention in conversation history
- 2048 token context limit

## Author

**OsamaBinLikhon**

## License

Apache 2.0
### Framework versions

- PEFT 0.18.0