trlm-stage-1-sft-final-2

🧠 trlm-stage-1-sft-final-2

trlm-stage-1-sft-final-2 is the Stage 1 post-training model for the Tiny Reasoning Language Model (trlm) project.
This stage focuses on everyday conversations and general instruction following, fine-tuned on a curated dataset of 58,000 entries.

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📖 Model Description

• Base Model: HuggingFaceTB/SmolLM2-135M-Instruct
• Type: Causal Language Model (decoder-only transformer)
• Stage: Post-training Stage 1 (SFT)
• Objective: Build a solid foundation in instruction-following and dialogue coherence before advancing to reasoning-specific training.

This stage teaches the model to follow instructions, rewrite, summarize, and hold conversations without reasoning tokens.

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🎯 Intended Uses & Limitations

Intended Uses

• Everyday conversation assistants
• Instruction-following tasks (summarization, rewriting, simple dialogue)
• Precursor foundation for reasoning post-training (Stage 2+)

Limitations

• Not optimized for reasoning (handled in later stages)
• May struggle with multi-step logical or mathematical problems
• Trained only on English datasets

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📊 Training Data

This model was trained on the dataset:
👉 Shekswess/trlm-sft-stage-1-final

Dataset summary:

• Entries: 58,000
• Sources: 7 HuggingFaceTB/smoltalk2 subsets
• Focus: Non-reasoning conversations and instruction-following

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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: 2
• 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-1-sft-final-2"

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

# Example inference
inputs = tokenizer("Write a short daily affirmation:", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

📌 Next Steps

• Stage 2: Supervised fine-tuning with reasoning-focused data
• Stage 3: DPO / preference optimization for reasoning stability

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