---
license: apache-2.0
base_model: Qwen/Qwen3-1.7B
datasets:
  - ericholam/codeforces-sft-dataset-beta
  - TeichAI/claude-4.5-opus-high-reasoning-250x
language:
  - en
tags:
  - code
  - reasoning
  - competitive-programming
  - sft
pipeline_tag: text-generation
library_name: transformers
---

# Qwen3-1.7B-Sushi-Coder

A fine-tuned Qwen3-1.7B model optimized for code generation and competitive programming.

## Model Details

- **Base Model:** [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B)
- **Fine-tuning Method:** SFT with LoRA (merged)
- **Training Steps:** 1000
- **Context Length:** 2048

## Training

This model was fine-tuned using:
- **LoRA** (r=8, alpha=16) on attention and MLP layers
- **Liger Kernel** for memory efficiency
- **Packing** with FlashAttention-2
- **Cosine learning rate schedule** (2e-5 peak)

### Datasets

- [ericholam/codeforces-sft-dataset-beta](https://huggingface.co/datasets/ericholam/codeforces-sft-dataset-beta) - 1408 competitive programming examples
- [TeichAI/claude-4.5-opus-high-reasoning-250x](https://huggingface.co/datasets/TeichAI/claude-4.5-opus-high-reasoning-250x) - High-quality reasoning examples

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "bigatuna/Qwen3-1.7B-Sushi-Coder",
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("bigatuna/Qwen3-1.7B-Sushi-Coder")

messages = [
    {"role": "user", "content": "Write a Python function to solve the two-sum problem."}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.6, top_p=0.95, top_k=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Sampling Parameters

For best results with Qwen3 models:
- **Temperature:** 0.6-0.7
- **Top-p:** 0.95
- **Top-k:** 20
- **Do not use greedy decoding** (temp=0 causes repetitions)

## License

Apache 2.0