---
license: apache-2.0
datasets:
- Sidsidney/OpenThoughts-114k
- teknium/OpenHermes-2.5
language:
- en
- zh
base_model:
- Qwen/Qwen3-0.6B
tags:
- qwen
- ties
- merge
- finetune
- small
- thinking
- uncensored
- coral
pipeline_tag: 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](https://github.com/arcee-ai/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 |

```yaml
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](https://huggingface.co/datasets/teknium/OpenHermes-2.5) (simple QA + instruction following)
- **500 rows** — [OpenThoughts](https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k) (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 |