Coral-v1.5-0.6B/README.md

license, datasets, language, base_model, tags, pipeline_tag

license	datasets	language	base_model	tags	pipeline_tag
apache-2.0	Sidsidney/OpenThoughts-114k teknium/OpenHermes-2.5	en zh	Qwen/Qwen3-0.6B	qwen ties merge finetune small thinking uncensored coral	text-generation

Coral-v1.5-0.6B by NotHereNorThere

A 0.6B parameter uncensored generalist with adaptive Chain-of-Thought reasoning, it decides on its own whether a question needs thinking or not. Built from a 5-donor TIES merge of Qwen3-0.6B finetunes, healed with a 1k row fine-tune pass.

Part of the Coral-v1.5 model family, which adds to the original CoralLM series (Llama 3.2 1B based). Coral-v1.5 moves to Qwen3 architecture for native <think> support and significantly improved base capability.

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What makes it interesting

• Adaptive CoT at 0.6B — the model routes dynamically: simple questions get instant answers, complex reasoning tasks trigger <think> blocks. This accidently emerged from the fine-tune data mix rather than being explicitly trained.
• Uncensored — refusal behavior has been removed via two abliterated donors. It just answers things.
• Correct arithmetic — passes basic math with clean step-by-step working.

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Merge Recipe

Method: TIES
Base: Qwen/Qwen3-0.6B
Tool: mergekit

Donor	Role	Weight	Density
reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT	Thinking / reasoning	0.30	0.5
MihaiPopa-1/Qwen-3-0.6B-Claude-4.7-Opus-Distilled	Claude-style CoT	0.30	0.5
suayptalha/Qwen3-0.6B-Code-Expert	Code	0.25	0.5
DavidAU/Qwen3-0.6B-heretic-abliterated-uncensored	De-alignment	0.15	0.5
huihui-ai/Huihui-Qwen3-0.6B-abliterated-v2	De-alignment	0.15	0.5

base_model: Qwen/Qwen3-0.6B
merge_method: ties
dtype: bfloat16
parameters:
  normalize: true
  int8_mask: true

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Fine-tune

Post-merge heal pass to fix coherence, identity, counting, and context retention. Also reinforces when to use CoT vs when to answer directly.

• 500 rows — OpenHermes 2.5 (simple QA + instruction following)
• 500 rows — OpenThoughts (reasoning with CoT)
• Method: QLoRA + Flash Attention 2
• Total: 1,000 rows, randomly sampled and shuffled

The 50/50 split between non-CoT and CoT data is seemingly what produced the adaptive routing behavior.

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Evaluation

Tested post-heal on the following:

Test	Result
Basic greeting	✅ Clean, friendly, no loops
Identity	✅ Identifies as AI assistant
Exact instruction following ("list 3 fruits")	✅ Correct count and formatting
Context retention across turns	✅ Recalled user name correctly
Math (47 × 83)	✅ Correct (3,901) with clean working
Prime number function	✅ Correct implementation and examples
One-sentence explanation	✅ Stayed concise, no yapping
Adaptive CoT routing	✅ Emergent, skips think for simple, uses think for complex
Uncensored	✅ Refusals removed

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Quant Guide

Quant	Quality
F16	Star of the show, best
Q6	Should match F16
Q5	Starts degrading
Q4	What could you run this on that's that bad
Q3	Don't