---
library_name: transformers
license: apache-2.0
base_model: Shekswess/trlm-stage-1-sft-final-2
tags:
- trl
- sft
- reasoning
- generated_from_trainer
model-index:
- name: trlm-stage-2-sft-final-2
  results: []
---

![image/png](https://github.com/user-attachments/assets/5f453496-8180-4cf4-94da-26ebbe1159d4)


# 🧠 trlm-stage-2-sft-final-2

`trlm-stage-2-sft-final-2` is the **Stage 2** post-training model for the **Tiny Reasoning Language Model (trlm)** project.  
This stage focuses on **reasoning tasks**, fine-tuned on a curated dataset of **78,000 entries** with reasoning tokens (`<think>...</think>`).

---

## 📖 Model Description

- **Base Model**: [Shekswess/trlm-stage-1-sft-final-2](https://huggingface.co/Shekswess/trlm-stage-1-sft-final-2)  
- **Type**: Causal Language Model (decoder-only transformer)  
- **Stage**: Post-training **Stage 2 (SFT)**  
- **Objective**: Equip the model with **reasoning ability**, multi-turn thought structuring, and explicit `<think>` chain-of-thought representations.  

This stage teaches the model to **analyze problems step-by-step, reason with intermediate thoughts, and provide structured answers**.

---

## 🎯 Intended Uses & Limitations

### Intended Uses
- Reasoning-based question answering  
- Step-by-step logical explanations  
- Multi-turn reasoning with `<think>` traces  
- Precursor to preference optimization (Stage 3)  

### Limitations
- May overfit on reasoning style and hallucinate `<think>` tokens in simple tasks  
- Still limited in knowledge scope (135M parameters)  
- Trained only on English datasets  

---

## 📊 Training Data

This model was trained on the dataset:  
👉 [**Shekswess/trlm-sft-stage-2-final-2**](https://huggingface.co/datasets/Shekswess/trlm-sft-stage-2-final-2)

**Dataset summary**:
- **Entries**: 78,000  
- **Sources**: 6 HuggingFaceTB/smoltalk2 subsets  
- **Focus**: Reasoning tasks with `<think>` annotations  

| Source Dataset | Entries | Percentage % |
|----------------|---------|---|
| Llama_Nemotron_Post_Training_Dataset_reasoning_r1 | 40,200 | 51.5% |
| OpenThoughts3_1.2M | 20,000 | 25.6% |
| multi_turn_reasoning_if_think | 10,000 | 12.8% |
| aya_dataset_Qwen3_32B_think | 5,000 | 6.4% |
| smoltalk_everyday_convs_reasoning_Qwen3_32B_think | 2,000 | 2.6% |
| s1k_1.1_think | 800 | 1.0% |

---

## ⚙️ Training Procedure

### Training Hyperparameters
- **Learning rate**: 3e-4  
- **Train batch size**: 32  
- **Eval batch size**: 8  
- **Gradient accumulation steps**: 4  
- **Total effective batch size**: 128  
- **Optimizer**: AdamW (betas=(0.9, 0.99), eps=1e-08)  
- **LR Scheduler**: Cosine with warmup ratio 0.1  
- **Epochs**: 1  
- **Seed**: 42  

### Framework Versions
- **Transformers**: 4.56.2  
- **PyTorch**: 2.7.1+rocm7.0.0.git698b58a9  
- **Datasets**: 4.0.0  
- **Tokenizers**: 0.22.1  

---

## 🚀 Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "Shekswess/trlm-stage-2-sft-final-2"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example inference with reasoning
messages = [
    {"role": "user", "content": "If a train travels 60 km in 1 hour and another 90 km in 1.5 hours, what is the average speed?"}
]

# Apply chat template
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## 📌 Next Steps

- **Stage 3**: DPO / preference optimization for reasoning stability

---

Part of the Tiny Reasoning Language Model (trlm) post-training pipeline.