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Model: Shekswess/trlm-stage-2-sft-final-2
Source: Original Platform

README.md

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+---
+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.