license, datasets, language, base_model, tags, pipeline_tag
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| apache-2.0 |
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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.
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.
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
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.
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 |
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 |