Shekswess/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.

📖 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.

🎯 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

📊 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

⚙️ 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

🚀 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))

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