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Model: aadityabuilds/qwen2-5-coder-7b-kernelbook-sft-equal-tokens
Source: Original Platform

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: Qwen/Qwen2.5-Coder-7B-Instruct
+tags:
+- triton
+- kernelbook
+- code-generation
+- sft
+- trl
+- text-generation
+datasets:
+- custom
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# Qwen2.5-Coder-7B KernelBook SFT (equal tokens)
+
+**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.
+
+This repo is the **equal-exposure** SFT checkpoint (`checkpoint-350`, ~1.06 epochs) selected to match SDFT's one-epoch training for fair comparison.
+
+## Method
+
+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.
+
+## Dataset
+
+- **KernelBook** — PyTorch module prompts paired with reference Triton kernels
+- Deduplicated, filtered to completions ≤4096 tokens, repo-stratified 80/10/10 split
+- Stopped at **checkpoint-350** (~1.06 epochs) for parity with the SDFT run
+
+## Intended use
+
+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.
+
+## Quick start
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "aadityabuilds/qwen2-5-coder-7b-kernelbook-sft-equal-tokens"
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, torch_dtype="auto", device_map="auto", trust_remote_code=True
+)
+
+messages = [
+    {
+        "role": "user",
+        "content": "Convert the following PyTorch code to an equivalent Triton kernel...",
+    }
+]
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=1200, do_sample=False)
+print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1] :], skip_special_tokens=True))
+```
+
+## Training summary
+
+| Setting | Value |
+|---------|-------|
+| Base model | Qwen2.5-Coder-7B-Instruct |
+| Method | SFT (TRL `SFTTrainer`, completion-only NLL) |
+| Checkpoint | `checkpoint-350` (~1.06 epochs) |
+| Hardware | 4× H100 (Modal) |
+| Parallelism | DeepSpeed ZeRO-3, bf16 |
+
+## Limitations
+
+Specialized for KernelBook Triton codegen. May show reduced performance on general coding, math, and knowledge benchmarks compared to the base instruct model.