a107bbde87
Model: aadityabuilds/qwen2-5-coder-7b-kernelbook-sft-equal-tokens Source: Original Platform
75 lines
2.6 KiB
Markdown
75 lines
2.6 KiB
Markdown
---
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library_name: transformers
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license: apache-2.0
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base_model: Qwen/Qwen2.5-Coder-7B-Instruct
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tags:
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- triton
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- kernelbook
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- code-generation
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- sft
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- trl
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- text-generation
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datasets:
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- custom
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language:
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- en
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pipeline_tag: text-generation
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---
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# Qwen2.5-Coder-7B KernelBook SFT (equal tokens)
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**Supervised Fine-Tuning (SFT)** checkpoint of [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct), post-trained on the **KernelBook** Triton kernel dataset.
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This repo is the **equal-exposure** SFT checkpoint (`checkpoint-350`, ~1.06 epochs) selected to match SDFT's one-epoch training for fair comparison.
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## Method
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This model was trained with **SFT** using TRL's `SFTTrainer`: standard next-token prediction on chat-formatted prompt → Triton completion pairs, with completion-only loss (prompt tokens masked). Training used DeepSpeed ZeRO-3 and bf16 on Modal.
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## Dataset
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- **KernelBook** — PyTorch module prompts paired with reference Triton kernels
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- Deduplicated, filtered to completions ≤4096 tokens, repo-stratified 80/10/10 split
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- Stopped at **checkpoint-350** (~1.06 epochs) for parity with the SDFT run
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## Intended use
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Generate Triton GPU kernels from PyTorch-style module descriptions. Best for KernelBook-style conversion prompts; not evaluated as a general-purpose chat or reasoning model.
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## Quick start
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "aadityabuilds/qwen2-5-coder-7b-kernelbook-sft-equal-tokens"
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_id, torch_dtype="auto", device_map="auto", trust_remote_code=True
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)
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messages = [
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{
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"role": "user",
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"content": "Convert the following PyTorch code to an equivalent Triton kernel...",
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}
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]
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=1200, do_sample=False)
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print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1] :], skip_special_tokens=True))
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```
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## Training summary
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| Setting | Value |
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|---------|-------|
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| Base model | Qwen2.5-Coder-7B-Instruct |
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| Method | SFT (TRL `SFTTrainer`, completion-only NLL) |
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| Checkpoint | `checkpoint-350` (~1.06 epochs) |
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| Hardware | 4× H100 (Modal) |
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| Parallelism | DeepSpeed ZeRO-3, bf16 |
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## Limitations
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Specialized for KernelBook Triton codegen. May show reduced performance on general coding, math, and knowledge benchmarks compared to the base instruct model.
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