Grifflet-0.6B

Developed by: Daemontatox License: Apache-2.0 Base Model: Daemontatox/Grifflet-0.6B

Model Overview

Grifflet-0.6B is a lightweight, fine-tuned transformer model designed for efficient reasoning, math problem-solving, and code generation. Despite its small size (600 million parameters), it delivers strong performance for structured tasks requiring logical coherence, step-by-step thinking, and multi-turn conversations.

This model is optimized using TRL and LoRA with Unsloth acceleration for improved speed and memory efficiency.

Training Dataset

• Dataset: OpenThoughts2-1M
• Size: ~1.1M high-quality samples
• Content Focus: Stepwise reasoning, logic puzzles, math proofs, structured code generation, educational conversations
• Tools: Curator Viewer

The dataset builds on OpenThoughts-114k and incorporates samples from OpenR1-Math, KodCode, and other logic-focused corpora.

Intended Use Cases

• Educational chatbots for math and programming
• AI agents requiring clear step-by-step reasoning
• Code generation tools for simple to intermediate logic
• Lightweight deployments on resource-constrained hardware

Known Limitations

• Primarily trained on English; limited multilingual support
• May hallucinate or generate incorrect factual content
• Performance may decline on abstract or high-complexity queries due to model size

Quick Example

from transformers import pipeline

pipe = pipeline("text-generation", model="Daemontatox/Grifflet-0.6B")
response = pipe("What is the derivative of x^2?")
print(response[0]['generated_text'])

Technical Training Details

• Framework: TRL + LoRA with Unsloth acceleration
• Training Volume: ~1M samples
• Hardware: A100 80GB or equivalent GPU
• Objective: Enable coherent, structured reasoning under constrained compute budgets

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