102 lines
3.3 KiB
Markdown
102 lines
3.3 KiB
Markdown
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---
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license: apache-2.0
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datasets:
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- Sidsidney/OpenThoughts-114k
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- teknium/OpenHermes-2.5
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language:
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- en
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- zh
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base_model:
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- Qwen/Qwen3-0.6B
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tags:
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- qwen
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- ties
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- merge
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- finetune
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- small
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- thinking
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- uncensored
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- coral
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pipeline_tag: text-generation
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---
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# Coral-v1.5-0.6B by NotHereNorThere
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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.
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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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---
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## What makes it interesting
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- **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.
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- **Uncensored** — refusal behavior has been removed via two abliterated donors. It just answers things.
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- **Correct arithmetic** — passes basic math with clean step-by-step working.
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---
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## Merge Recipe
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**Method:** TIES
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**Base:** `Qwen/Qwen3-0.6B`
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**Tool:** [mergekit](https://github.com/arcee-ai/mergekit)
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| Donor | Role | Weight | Density |
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|---|---|---|---|
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| `reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT` | Thinking / reasoning | 0.30 | 0.5 |
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| `MihaiPopa-1/Qwen-3-0.6B-Claude-4.7-Opus-Distilled` | Claude-style CoT | 0.30 | 0.5 |
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| `suayptalha/Qwen3-0.6B-Code-Expert` | Code | 0.25 | 0.5 |
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| `DavidAU/Qwen3-0.6B-heretic-abliterated-uncensored` | De-alignment | 0.15 | 0.5 |
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| `huihui-ai/Huihui-Qwen3-0.6B-abliterated-v2` | De-alignment | 0.15 | 0.5 |
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```yaml
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base_model: Qwen/Qwen3-0.6B
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merge_method: ties
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dtype: bfloat16
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parameters:
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normalize: true
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int8_mask: true
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```
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---
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## Fine-tune
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Post-merge heal pass to fix coherence, identity, counting, and context retention. Also reinforces when to use CoT vs when to answer directly.
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- **500 rows** — [OpenHermes 2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5) (simple QA + instruction following)
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- **500 rows** — [OpenThoughts](https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k) (reasoning with CoT)
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- **Method:** QLoRA + Flash Attention 2
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- **Total:** 1,000 rows, randomly sampled and shuffled
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The 50/50 split between non-CoT and CoT data is seemingly what produced the adaptive routing behavior.
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---
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## Evaluation
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Tested post-heal on the following:
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| Test | Result |
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|---|---|
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| Basic greeting | ✅ Clean, friendly, no loops |
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| Identity | ✅ Identifies as AI assistant |
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| Exact instruction following ("list 3 fruits") | ✅ Correct count and formatting |
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| Context retention across turns | ✅ Recalled user name correctly |
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| Math (47 × 83) | ✅ Correct (3,901) with clean working |
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| Prime number function | ✅ Correct implementation and examples |
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| One-sentence explanation | ✅ Stayed concise, no yapping |
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| Adaptive CoT routing | ✅ Emergent, skips think for simple, uses think for complex |
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| Uncensored | ✅ Refusals removed |
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---
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## Quant Guide
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| Quant | Quality |
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|---|---|
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| F16 | Star of the show, best |
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| Q6 | Should match F16 |
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| Q5 | Starts degrading |
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| Q4 | What could you run this on that's that bad |
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| Q3 | Don't |
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