Add model card

README.md

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+---
+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
+- **Training 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