🧠 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>).

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📖 Model Description

• Base Model: 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.

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🎯 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

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📊 Training Data

This model was trained on the dataset:
👉 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%

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⚙️ 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

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🚀 Usage

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

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