---
base_model: meta-llama/Llama-3.2-3B-Instruct
library_name: transformers
license: llama3.2
pipeline_tag: text-generation
tags:
- sft
- scientific-reasoning
- instruction-tuning
- open-instruct
---

# Divij/Llama-3.2-3B-Instruct-sft-with-thoughts

Supervised fine-tune of [`meta-llama/Llama-3.2-3B-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on a scientific-methodology
instruction dataset, where each assistant response interleaves `<Thought_i>` reasoning with `<Step_i>` actions.

The project goal is to compare whether including explicit `<Thought_i>` reasoning
traces alongside each `<Step_i>` action during SFT produces stronger scientific-methodology
generators than training on step-only plans.

## Variant

This checkpoint is the **with-thoughts** variant:
The assistant target alternates `<Thought_i>` / `<Step_i>` pairs, so the model learns to produce explicit reasoning before each action. Trained with `max_seq_length=6144` to fit the longer sequences.

## Training data

- Source: `sft_with_thoughts.jsonl` from the `verl_scientific_discovery`
  repeated-sampling pipeline.
- 4,990 `messages`-format examples (`system` + `user` + `assistant`).
- Each assistant response is a step-by-step research methodology for a given
  `Research Goal` + `Constraints` prompt.

## Training setup

- **Framework:** [open-instruct](https://github.com/allenai/open-instruct) `finetune.py` (accelerate + FSDP2).
- **Hardware:** 2× NVIDIA H100 NVL (96 GB).
- **Precision:** bf16 mixed precision.
- **Attention:** FlashAttention-2.
- **Memory:** gradient checkpointing enabled.

### Hyperparameters

| | |
|---|---|
| `max_seq_length` | **6144** |
| `num_train_epochs` | 3 |
| `per_device_train_batch_size` | 1 |
| `gradient_accumulation_steps` | 8 |
| Effective batch size | 16 (1 × 2 GPU × 8 accum) |
| `learning_rate` | 2e-5 |
| `lr_scheduler_type` | linear |
| `warmup_ratio` | 0.03 |
| `weight_decay` | 0.0 |
| `seed` | 42 |
| Optimizer | fused AdamW |
| Total optimization steps | 936 |
| **Final training loss** | **0.839** |

The chat template is inherited from the base model
(`meta-llama/Llama-3.2-3B-Instruct`). Labels are masked on the `system` and
`user` turns so only the assistant response contributes to the loss
(open-instruct's `sft_tulu_tokenize_and_truncate_v1` transform).

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "Divij/Llama-3.2-3B-Instruct-sft-with-thoughts"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
    repo,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are an expert research scientist. Produce reasoning/action pairs: <Thought_i> followed by <Step_i>."},
    {"role": "user", "content": (
        "You are given a scientific research problem.\n\n"
        "Research Goal:\n<your research goal here>\n\n"
        "Constraints:\n1) <constraint 1>\n2) <constraint 2>"
    )},
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)

output = model.generate(
    inputs,
    max_new_tokens=1024,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
)
print(tokenizer.decode(output[0][inputs.shape[-1]:], skip_special_tokens=True))
```

## Notes

- **Context length.** Use `max_seq_length` ≥ **6144** at inference time to match
  the training regime; generations longer than this were not seen during training.
- **Intended use.** Research artifact for generating structured scientific research
  plans. Not aligned for general-purpose chat or safety-critical use.
- **Compared to sibling.** A matching **without-thoughts** checkpoint at
  [`Divij/Llama-3.2-3B-Instruct-sft-without-thoughts`](https://huggingface.co/Divij/Llama-3.2-3B-Instruct-sft-without-thoughts) is trained on
  the same data but with the opposite treatment of reasoning traces.