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	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

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

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 (simple QA + instruction following)
• 1,250 rows — OpenThoughts (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