114 lines
3.8 KiB
Markdown
114 lines
3.8 KiB
Markdown
---
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base_model: Qwen/Qwen2.5-3B-Instruct
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library_name: transformers
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license: other
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pipeline_tag: text-generation
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tags:
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- sft
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- scientific-reasoning
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- instruction-tuning
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- open-instruct
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---
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# Divij/Qwen2.5-3B-Instruct-sft-without-thoughts
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Supervised fine-tune of [`Qwen/Qwen2.5-3B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) on a scientific-methodology
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instruction dataset, where each assistant response is a plain step-by-step research methodology.
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The project goal is to compare whether including explicit `<Thought_i>` reasoning
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traces alongside each `<Step_i>` action during SFT produces stronger scientific-methodology
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generators than training on step-only plans.
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## Variant
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This checkpoint is the **without-thoughts** variant:
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The assistant target is only `<Step_1>...</Step_1>` ... `<Step_n>...</Step_n>` — the reasoning traces are excluded. Trained with `max_seq_length=2048`.
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## Training data
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- Source: `sft_without_thoughts.jsonl` from the `verl_scientific_discovery`
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repeated-sampling pipeline.
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- 4,990 `messages`-format examples (`system` + `user` + `assistant`).
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- Each assistant response is a step-by-step research methodology for a given
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`Research Goal` + `Constraints` prompt.
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## Training setup
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- **Framework:** [open-instruct](https://github.com/allenai/open-instruct) `finetune.py` (accelerate + FSDP2).
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- **Hardware:** 2× NVIDIA H100 NVL (96 GB).
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- **Precision:** bf16 mixed precision.
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- **Attention:** FlashAttention-2.
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- **Memory:** gradient checkpointing enabled.
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### Hyperparameters
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| | |
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|---|---|
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| `max_seq_length` | **2048** |
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| `num_train_epochs` | 3 |
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| `per_device_train_batch_size` | 1 |
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| `gradient_accumulation_steps` | 8 |
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| Effective batch size | 16 (1 × 2 GPU × 8 accum) |
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| `learning_rate` | 2e-5 |
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| `lr_scheduler_type` | linear |
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| `warmup_ratio` | 0.03 |
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| `weight_decay` | 0.0 |
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| `seed` | 42 |
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| Optimizer | fused AdamW |
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| Total optimization steps | 936 |
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| **Final training loss** | **2.054** |
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The chat template is inherited from the base model
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(`Qwen/Qwen2.5-3B-Instruct`). Labels are masked on the `system` and
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`user` turns so only the assistant response contributes to the loss
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(open-instruct's `sft_tulu_tokenize_and_truncate_v1` transform).
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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repo = "Divij/Qwen2.5-3B-Instruct-sft-without-thoughts"
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tokenizer = AutoTokenizer.from_pretrained(repo)
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model = AutoModelForCausalLM.from_pretrained(
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repo,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [
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{"role": "system", "content": "Given a research goal and constraints, provide a step-by-step methodology.\n\nFormat:\n<Step_1>...</Step_1>\n<Step_2>...</Step_2>"},
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{"role": "user", "content": (
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"You are given a scientific research problem.\n\n"
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"Research Goal:\n<your research goal here>\n\n"
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"Constraints:\n1) <constraint 1>\n2) <constraint 2>"
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)},
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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return_tensors="pt",
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).to(model.device)
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output = model.generate(
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inputs,
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max_new_tokens=1024,
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do_sample=True,
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temperature=0.7,
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top_p=0.9,
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)
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print(tokenizer.decode(output[0][inputs.shape[-1]:], skip_special_tokens=True))
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```
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## Notes
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- **Context length.** Use `max_seq_length` ≥ **2048** at inference time to match
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the training regime; generations longer than this were not seen during training.
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- **Intended use.** Research artifact for generating structured scientific research
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plans. Not aligned for general-purpose chat or safety-critical use.
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- **Compared to sibling.** A matching **with-thoughts** checkpoint at
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[`Divij/Qwen2.5-3B-Instruct-sft-with-thoughts`](https://huggingface.co/Divij/Qwen2.5-3B-Instruct-sft-with-thoughts) is trained on
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the same data but with the opposite treatment of reasoning traces.
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