trlm-stage-3-dpo-final-2

🧠 trlm-stage-3-dpo-final-2

trlm-stage-3-dpo-final-2 is the Stage 3 post-training model for the Tiny Reasoning Language Model (trlm) project.
This stage focuses on preference alignment using Direct Preference Optimization (DPO) with 50k preference pairs.

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📖 Model Description

• Base Model: Shekswess/trlm-stage-2-sft-final-2
• Type: Causal Language Model (decoder-only transformer)
• Stage: Post-training Stage 3 (DPO)
• Objective: Align model outputs with human-preferred reasoning and answers by contrasting chosen vs rejected completions.

This stage improves the model’s alignment, coherence, and reasoning stability.

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🎯 Intended Uses & Limitations

Intended Uses

• Aligned reasoning assistant with structured <think> traces
• Multi-turn reasoning with preference-optimized outputs
• Safer, more useful responses for reasoning tasks

Limitations

• Trained only on preference data → may inherit biases from source datasets
• Limited parameter count (135M) restricts knowledge breadth
• Still prone to hallucinations under complex reasoning chains

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📊 Training Data

This model was trained on the dataset:
👉 Shekswess/trlm-dpo-stage-3-final-2

Dataset summary:

• Entries: 50,000 preference pairs
• Source: scottgeng00/olmo-3-preference-mix-deltas_reasoning-yolo_scottmix-DECON-chfiltered
• Focus: Preference alignment with chosen vs rejected responses

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⚙️ Training Procedure

Training Hyperparameters

• Learning rate: 1e-5
• Train batch size: 32
• Eval batch size: 8
• Gradient accumulation steps: 4
• Total effective batch size: 128
• Optimizer: AdamW (betas=(0.9, 0.999), eps=1e-08)
• LR Scheduler: Cosine with minimum LR + 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-3-dpo-final-2"

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

# Example inference with preference-aligned reasoning
messages = [
    {"role": "user", "content": "Explain why the sky is blue in simple terms."}
]

# 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))

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