---
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B-Instruct
tags:
- triton
- kernel
- code-generation
- fine-tuned
datasets:
- triton-kernels-6k
language:
- en
pipeline_tag: text-generation
---

# 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

```python
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