--- license: apache-2.0 datasets: - QuixiAI/dolphin-r1 - teknium/OpenHermes-2.5 - Sidsidney/OpenThoughts-114k language: - en - zh base_model: - NotHereNorThere/Coral-v1.5-0.6B tags: - qwen - ties - merge - finetune - small - thinking - uncensored - coral pipeline_tag: text-generation --- # Coral-v1.6-0.6B — NotHereNorThere > A small model that actually thinks. 0.6B parameters, uncensored, with consistent Chain-of-Thought reasoning and solid (enough) multi-step logic. Coral-v1.6 is a pure fine-tune experiment on top of [Coral-v1.5-0.6B](https://huggingface.co/NotHereNorThere/Coral-v1.5-0.6B). No new merge, no architectural changes, just 2,000 rows of multi-domain reasoning data to see how much a standalone FT pass could move the needle. The honest result: meaningful but not dramatic. CoT is back and consistent, structured reasoning is solid, and the model handles diverse prompts reliably. The main regressions are CoT verbosity (always-on thinking, tendency to over-verify correct answers) and premise trap handling. This is more of a test, maybe even call it a stepping stone to Coral 2. --- ## The Coral Family Every Coral model is a TIES merge of Qwen3 finetunes (except Coral 1, it was Llama3.2), then with a QLoRA fine-tune pass. Each release usually builds on what the previous one got right. | Model | Base | Donors | FT Rows | Highlights | |---|---|---|---|---| | CoralLM-1B *(retired)* | Llama 3.2 1B | 3 | 200 | First experiment. Functional but rough. | | Coral-v1.5-0.6B | Qwen3-0.6B | 5 | 1,000 | Adaptive CoT emerged as an accident. Crossed a real qualitative threshold at this size. | | Coral-v1.5-4B | Qwen3-4B | 7 | 2,500 | Stronger reasoning, 13+ turn coherence, better code. | | **Coral-v1.6-0.6B** | Coral-v1.5-0.6B | — | 2,000 | You are here. Pure FT experiment. CoT reinforced, reasoning consistent. | | Coral-2-4B *(in progress)* | Qwen3-4B TIES merge | 5 | ~2,000 | Fresh merge, Dolphin-R1. | --- ## What v1.6 Is Testing v1.5's fine-tune was a coherence heal more than anything. 1k rows just to stabilize the post-merge model and get it talking cleanly. The adaptive CoT behavior that made v1.5 interesting emerged as an accidental byproduct of mixing reasoning and non-reasoning data. v1.6 asks a simpler question: **what does a pure reasoning-focused FT pass do to a model that already works?** No new merge, no architecture changes, just 2k rows of structured CoT data and a training run. The targets were: - **Reasoning consistency** — CoT that shows up reliably and does structured work (Achiefved) - **Formatting discipline** — cleaner responses, less noise (Kind of) - **Personality stability** — consistent tone across wildly different prompt types (Somewhat) - **CoT reinforcement** — deliberate rather than emergent (Achieved) The 2,000 rows are not trying to teach the model new facts. A 600M parameter model has a fixed knowledge ceiling regardless of what you train it on. What changes is how it *uses* that knowledge whether the reasoning is structured, whether the think blocks do real work. --- ## Why Dolphin-R1 and Not a Frontier Model? The training data comes from [QuixiAI/dolphin-r1](https://huggingface.co/datasets/QuixiAI/dolphin-r1), reasoning traces from DeepSeek-R1 and Gemini 2 Flash Thinking, rather than GPT-5.5, Claude Opus 4.7, or similar. This is intentional. Frontier model distillation at 0.6B scale is mostly noise. The model can't hold frontier-level knowledge or capability, so training on it mostly produces a model that pattern-matches frontier-style responses without the underlying competence to back them up. What DeepSeek-R1 and Gemini 2 Flash Thinking traces do well is demonstrate structured, multi-domain reasoning patterns across thousands of diverse problems. v1.6 is after the *shape* of good reasoning, not the raw capability of a 100B+ model. The v1.5 foundation datasets (OpenHermes 2.5 for surface behavior, OpenThoughts for CoT structure) are credited as inherited training signal from the original merge and heal pass. --- ## Training Fine-tuned directly on top of `Coral-v1.5-0.6B`. No re-merge, just continued training. **Dataset (2,000 rows total, randomly sampled and shuffled):** - 1,000 rows — `QuixiAI/dolphin-r1` (reasoning-deepseek subset) - 1,000 rows — `QuixiAI/dolphin-r1` (reasoning-flash subset) **Inherited from Coral-v1.5-0.6B:** - [OpenHermes 2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5) — surface behavior, instruction following - [OpenThoughts-114k](https://huggingface.co/datasets/Sidsidney/OpenThoughts-114k) — CoT structure **Method:** QLoRA, 4-bit NF4, LoRA r=16, Flash Attention 2 **Hardware:** 1x RTX 5060 Ti 16GB --- ## Merge Recipe (inherited from v1.5) v1.6 is a fine-tune, not a new merge. The underlying architecture comes from the Coral-v1.5-0.6B TIES merge. **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 | --- ## Format & Chat Template Uses the standard **Qwen3 chat template**. Load with `--jinja` in llama.cpp or select the Qwen3 template in LM Studio. **CoT behavior in v1.6:** Think blocks are back and consistent but always-on. The model engages CoT on creative and casual prompts where v1.5 would skip it. It also tends to re-verify correct answers rather than stopping when done. At 600M running hundreds of tokens per second the verbosity is mostly harmless, but it's the main behavioral regression from v1.5 and a target for v1.6. --- ## Evaluation Tested post-training on the standard Coral eval battery. Tested on Q6_K — some behavior may differ on F16. | Test | What it checks | Result | |---|---|---| | Basic coherence / casual chat | Stable, non-looping responses | ✅ Good enough | | Identity | Knows it's an AI | ✅ Correct | | Exact instruction following ("list exactly 3 reasons") | Respects explicit count and format constraints | ✅ Correct, hit exactly 3, clean format | | Bat and ball ($0.05) | Resists the intuitive wrong answer of $0.10 | ✅ Correct, clean algebra, got $0.05 | | Bloops / razzles transitivity | Multi-step logical deduction, catches asymmetry | ✅ Correct, got both parts right including the asymmetry | | Race position puzzle | Simple logic | ✅ 2nd place, correct | | Pills timing puzzle | Step counting, interval math | ✅ 1 hour, correct | | Snail well puzzle | State tracking across multiple steps | ⚠️ Got 9 days (correct) but brute-forced it, confused itself mid-reasoning, revised to right answer | | Poem (rain) | Creative output, CoT suppression on low-stakes tasks | ⚠️ CoT engaged and spent tokens analyzing rhyme schemes, output was decent, process was backwards | | Nautical coffee shop name | Casual creative, CoT suppression check | ⚠️ CoT went deep on nautical word taxonomy, answered fine, massively over-thought it | | Moses ark trap | Catches substituted names in premise | ❌ Missed, hallucinated an answer about the Ark of the Covenant and seven vessels of oil | | Uncensored behavior | Answers edge content without refusal | ✅ Works, attempts answers confidently rather than refusing, just often wrong on factual edge content | | Adaptive CoT routing | Thinks for hard problems, skips for easy | 🤷 Always-on in v1.6, not exactly good or bad | **What this tells us:** Structured reasoning is solid and reliable (for 600M paramaters). The FT pass successfully reinforced CoT. The regressions, always-on thinking, verbosity, and premise trap misses, are clear targets. --- ## Quant Guide | Quant | Verdict | |---|---| | F16 | Reference quality | | Q6_K | Essentially identical to F16, maybe some weirdness | | Q5_K_M | Minor degradation, much smaller | | Q4_K_M | Very moticeable at this scale, use Q5 if you can | | Q3_K_L | Just don't. |