初始化项目，由ModelHub XC社区提供模型

Model: pavelslab-nyu/Llama-3.2-3B-ThinkSFT
Source: Original Platform

README.md

@@ -0,0 +1,55 @@
+---
+base_model: meta-llama/Llama-3.2-3B-Instruct
+license: llama3.2
+tags:
+  - sft
+  - math
+  - thinking
+  - llama
+---
+
+# Llama-3.2-3B-ThinkSFT
+
+`Llama-3.2-3B-Instruct` fine-tuned on 43.5K explicit reasoning traces
+from [OpenThoughts-114k](https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k)
+(math subset, thinking format). No continual pre-training.
+
+**Pipeline:** Base → Thinking SFT
+
+Released as part of:
+**[When Can LLMs Learn to Reason with Weak Supervision?](https://salmanrahman.net/rlvr-weak-supervision)**
+— Rahman, Shen, Mordvina, Palangi, Gabriel, Izmailov (2026)
+
+## Training Details
+
+| | |
+|---|---|
+| Init | `meta-llama/Llama-3.2-3B-Instruct` |
+| Data | [OpenThoughts-114k](https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k) math subset (43.5K examples) |
+| Epochs | 3 |
+| Sequence length | 8,192 |
+| Effective batch size | 256 sequences |
+| Learning rate | 1.5e-5, cosine decay, 10% warmup |
+| Optimizer | AdamW, weight decay 0.01 |
+| Precision | BF16 + Flash Attention 2 |
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("pavelslab-nyu/Llama-3.2-3B-ThinkSFT")
+tokenizer = AutoTokenizer.from_pretrained("pavelslab-nyu/Llama-3.2-3B-ThinkSFT")
+```
+
+## Citation
+
+```bibtex
+@article{rahman2026when,
+  title   = {When Can LLMs Learn to Reason with Weak Supervision?},
+  author  = {Rahman, Salman and Shen, Jingyan and Mordvina, Anna and
+             Palangi, Hamid and Gabriel, Saadia and Izmailov, Pavel},
+  journal = {Preprint},
+  year    = {2026}
+}
+```