A compact 373MB model that combines reasoning capability with natural conversational ability. Built by merging two independently fine-tuned variants of Qwen2.5-0.5B-Instruct using task arithmetic, then quantized to Q4_K_M for edge deployment.
Model Details
Base Model: Qwen/Qwen2.5-0.5B-Instruct
Parameters: 502M total
Quantization: Q4_K_M (GGUF)
File Size: ~373MB
RAM Required: ~500MB
Format: GGUF (llama.cpp / Ollama compatible)
Language: English
License: Apache 2.0
Training Overview
This model was built through a multi-stage process:
Stage 1 — Reasoning Distillation
Fine-tuned on 116k reasoning samples from Claude 4.6 Opus traces and DeepSeek-R1 generated chain-of-thought data. Config: LoRA r=128, lr=2e-4, 3 epochs, seq=4096.
Stage 2 — General + Factual + Anti-loop
Fresh fine-tune on 70k mixed samples covering general chat, factual QA, math, coding, and anti-loop correction pairs. Config: LoRA r=16, lr=7e-5, 1 epoch, seq=1024.
Stage 3 — Merge
Both models merged using task arithmetic (60% reasoning + 40% general) via mergekit to combine strengths of both stages.
