cdreetz/kwen2.5-1.5b

Triton Kernel Code Generation Model

This model is a fine-tuned version of Qwen/Qwen2.5-1.5B-Instruct specialized for generating Triton GPU kernels.

Model Details

• Base Model: Qwen/Qwen2.5-1.5B-Instruct
• Fine-tuned on: 6000 examples of Triton kernel code
• Eval Loss: 0.20
• Eval Perplexity: 1.22

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("cdreetz/kwen2.5-1.5b")
tokenizer = AutoTokenizer.from_pretrained("cdreetz/kwen2.5-1.5b")

prompt = "Write a Triton kernel for element-wise addition:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Training Details

• Epochs: 2
• Batch Size: 2
• Learning Rate: 1e-5
• Dataset Size: 6000 examples

Performance

The model generates syntactically correct Triton kernels with proper:

• @triton.jit decorators
• Kernel function signatures
• Launch function implementations
• Memory access patterns
• Grid configurations

Limitations

• Specialized for Triton kernel generation only
• May require prompt engineering for optimal results
• Generated kernels should be tested before production use