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

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 (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

NotHereNorThere/Coral-v1.5-4b

Coral-v1.5-4B