Shekswess/trlm-stage-2-sft-final-2

🧠 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
• —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**

Dataset summary:

• —Entries: 78,000
• —Sources: 6 HuggingFaceTB/smoltalk2 subsets
• —Focus: Reasoning tasks with <think> annotations

⚙️ 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

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.