初始化项目，由ModelHub XC社区提供模型

Model: Divij/Llama-3.2-3B-Instruct-sft-without-thoughts
Source: Original Platform

README.md

@@ -0,0 +1,113 @@
+---
+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-without-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 is a plain step-by-step research methodology.
+
+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 **without-thoughts** variant:
+The assistant target is only `<Step_1>...</Step_1>` ... `<Step_n>...</Step_n>` — the reasoning traces are excluded. Trained with `max_seq_length=2048`.
+
+## Training data
+
+- Source: `sft_without_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` | **2048** |
+| `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** | **1.988** |
+
+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-without-thoughts"
+tokenizer = AutoTokenizer.from_pretrained(repo)
+model = AutoModelForCausalLM.from_pretrained(
+    repo,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+messages = [
+    {"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>"},
+    {"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` ≥ **2048** 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 **with-thoughts** checkpoint at
+  [`Divij/Llama-3.2-3B-Instruct-sft-with-thoughts`](https://huggingface.co/Divij/Llama-3.2-3B-Instruct-sft-with-thoughts) is trained on
+  the same data but with the opposite treatment of reasoning traces.