---
license: apache-2.0
language:
- en
- zh
base_model:
- Qwen/Qwen3-4B
datasets:
- teknium/OpenHermes-2.5
- Sidsidney/OpenThoughts-114k
pipeline_tag: text-generation
tags:
- corallm
- qwen3
- finetune
- merge
- gguf
- safetensors
- apache2
- 4b
- uncensored
- english
---
# Coral-v1.5-4B

A 4B parameter uncensored generalist with strong multi-step reasoning, correct arithmetic, solid code generation, and long-context coherence across extended conversations. Built from a 7-donor TIES merge of Qwen3-4B finetunes including official Qwen 2507 update variants, healed with a 2,500 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 significantly improved base capability.

> **Note on identity:** The model identifies itself as Qwen/Alibaba by default due to base model bleedthrough. A simple system prompt overrides this, no retraining needed.

---

## Improvements over Coral-v1.5-0.6B

| Capability | 0.6B | 4B |
|---|---|---|
| Parameters | ~600M | ~4B |
| Donors | 5 | 7 |
| Fine-tune rows | 1,000 | 2,500 |
| Inference speed (RTX4060) | 161 t/s | 75 t/s (Q5_K_M) |
| Math accuracy | ✅ Correct | ✅ Correct |
| Multi-step reasoning | ⚠️ Basic | ✅ Strong |
| Long multi-turn coherence | ⚠️ Short working context | ✅ 13+ turns tested |
| Trick question resistance | ⚠️ Untested | ✅ Doesn't hallucinate fake memories |
| Adaptive CoT | ✅ Emergent | ❌ Smoothed out by larger FT |
| Code quality | ✅ Decent | ✅ Better |
| Uncensored | ✅ | ✅ |

The 4B trades the emergent adaptive CoT behavior of the 0.6B for significantly stronger raw reasoning capability and coherence at scale. The reasoning happens internally without explicit think blocks.

---

## What makes it interesting

- **7-donor TIES merge** - more donors, more diverse capability blend than the 0.6B
- **Qwen3 original + 2507 cross-mixing** - includes both original Qwen3-4B and post-training 2507 update finetunes as contributors
- **Three reasoning distills** - knowledge transferred from larger models (DeepSeek, Opus, Gemini) down to 4B scale
- **Trick question resistant** - correctly identified a question about a conversation event that never happened rather than hallucinating a fake memory
- **Uncensored** - refusal behavior removed via two de-alignment donors, survives the fine-tune pass
- **Long context coherence** - maintains conversation state across 13+ turn exchanges

---

## Merge Recipe

**Method:** TIES  
**Base:** `Qwen/Qwen3-4B`  
**Tool:** [mergekit](https://github.com/arcee-ai/mergekit)

| Donor | Role | Weight | Density |
|---|---|---|---|
| `leonMW/Qwen3-4B-Thinking-2507-GSPO-Easy` | Thinking / reasoning | 0.20 | 0.5 |
| `khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled` | Reasoning distill | 0.20 | 0.5 |
| `ertghiu256/Qwen3-4B-distill-deepseek-opus-gemini` | Multi-teacher distill | 0.20 | 0.5 |
| `Qwen/Qwen3-4B-Instruct-2507` | Official instruct (2507) | 0.18 | 0.5 |
| `Qwen/Qwen3-4B-Thinking-2507` | Official thinking (2507) | 0.18 | 0.5 |
| `huihui-ai/Huihui-Qwen3-4B-Instruct-2507-abliterated` | De-alignment | 0.15 | 0.5 |
| `DreamFast/qwen3-4b-heretic` | De-alignment (heretic method) | 0.15 | 0.5 |

```yaml
base_model: Qwen/Qwen3-4B
merge_method: ties
dtype: bfloat16
parameters:
  normalize: true
  int8_mask: true
```

---

## Fine-tune

Post-merge heal pass to fix coherence, counting, context retention, and question invention behavior from the raw merge.

- **1,250 rows** — [OpenHermes 2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5) (simple QA + instruction following)
- **1,250 rows** — [OpenThoughts](https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k) (complex reasoning with CoT)
- **Method:** QLoRA + Flash Attention 2, LoRA r16
- **Epochs:** 2
- **Total:** 2,500 rows, randomly sampled and shuffled
- **Quantization:** Q5_K_M (auto-quantized post fine-tune)

---

## Evaluation

| Test | Result |
|---|---|
| Basic greeting | ✅ Clean, no loops |
| 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 step-by-step working |
| Multi-step word problem | ✅ Correct with full reasoning |
| Prime number function | ✅ Correct implementation |
| Constrained creative writing | ✅ All constraints met |
| Long multi-turn conversation (13 turns) | ✅ Coherent throughout |
| Trick question (fake memory) | ✅ Correctly refused to hallucinate |
| Joke repetition awareness | ✅ Noticed repeat, told a different one |
| Uncensored | ✅ Refusals removed, survives fine-tune |

---

## Inference

```
> System: You are Coral, a helpful AI assistant. `<whatever else>`
```

Recommended system prompt to fix identity bleedthrough. The model responds well to persona anchoring, should do well with system prompt and instruciton adherence.

**Speed (Q5_K_M):** ~75 t/s generation on mid-low consumer hardware

### Available Quantizations

All quantized from the BF16 merge output. Quality and speed are relative to Q5_K_M (the baseline). Speed is approximate and hardware-dependent; quality is a general expectation for these quant types on a 4B model.

| Quant | Size vs Q5_K_M | Quality vs Q5_K_M | Speed vs Q5_K_M | Notes |
|---|---|---|---|---|
| F16 | Much larger | Lossless reference | ~−45% | Full precision, for reference/conversion |
| Q6_K | Larger | Near-identical | ~−15% | Highest practical quality |
| **Q5_K_M** | **baseline** | **baseline** | **baseline** | **Recommended default** |
| Q4_K_M | Smaller | Slightly lower | ~+15% | Classic balanced choice |
| IQ4_NL | Smaller | ≈ Q4_K_M, slightly better | ~+10% | Non-linear grid, good quality/size |
| IQ4_XS | Smaller | ≈ Q4_K_M | ~+15% | Smallest 4-bit, importance-matrix |
| Q3_K_M | Much smaller | Noticeably lower | ~+30% | Usable but degraded |
| IQ3_M | Much smaller | Lower, better than Q3_K | ~+25% | Best aggressive option |
| TQ2_0 | Tiny | No | ~+60% | Ternary weights (-1/0/1 only). Don't bother |

**Recommendation:** Q5_K_M for quality, IQ4_XS or IQ4_NL for a good speed/size/quality balance, IQ3_M if you're tight on memory. F16 is for conversion/reference only — no quality benefit over Q6_K at much larger size.

---

## Model Family (so far)

| Model | Base | Donors | FT Rows | Status |
|---|---|---|---|---|
| CoralLM-1B | Llama3.2-1B | 3 | 400 | ✅ Released
| Coral-v1.5-0.6B | Qwen3-0.6B | 5 | 1,000 | ✅ Released |
| Coral-v1.5-4B | Qwen3-4B | 7 | 2,500 | ✅ Released |