license, base_model, datasets, language, tags, pipeline_tag, library_name

license	base_model	datasets	language	tags	pipeline_tag	library_name
apache-2.0	Qwen/Qwen3-1.7B	ericholam/codeforces-sft-dataset-beta TeichAI/claude-4.5-opus-high-reasoning-250x	en	code reasoning competitive-programming sft	text-generation	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
• 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 - 1408 competitive programming examples
• TeichAI/claude-4.5-opus-high-reasoning-250x - High-quality reasoning examples

Usage

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