license: apache-2.0 base_model: huihui-ai/Huihui-Qwen3-Coder-30B-A3B-Instruct-abliterated tags:

gguf
uncensored
abliterated
code
moe
ollama

Huihui-Qwen3-Coder-30B-A3B-Instruct-abliterated-i1-GGUF — AI-conn fork

This is a fork, not our model. The abliterated weights are by huihui-ai; this GGUF conversion is by mradermacher; the base model is Qwen/Qwen3-Coder-30B-A3B-Instruct. This fork exists so the fleet's copy cannot be altered or pulled upstream, and to carry the fit notes below. License: Apache-2.0 (per the source repo).

Uncensored coder pick: MoE, 30B total / ~3B active per token — but note that all expert weights must sit in VRAM for GPU residency; "3B active" does not shrink the memory bill.

Recommended file for a 20 GB card: Huihui-Qwen3-Coder-30B-A3B-Instruct-abliterated.i1-Q4_K_M.gguf (17.28 GiB).

Ollama pull:

ollama pull hf.co/AI-conn/Huihui-Qwen3-Coder-30B-A3B-Instruct-abliterated-i1-GGUF:i1-Q4_K_M

Prime fit (measured budget, RX 7900 XT — 19.98 GiB VRAM, 32 GB RAM)

i1-Q4_K_M 17.28 GiB + small MoE KV + ~0.7 GiB reserve ≈ 18.3 GiB — resident, tight
i1-IQ4_XS (15.24 GiB) is the roomy option if you want more context headroom at slightly lower quant fidelity.
Independent datapoint (community harness): the stock base scores 60.4% on HumanEval Pro. No independent coding eval of any abliterated build exists yet — our fleet settles coder rankings with a local bake-off, not cards.

Huihui-Qwen3-Coder-30B-A3B-Instruct-abliterated-i1-GGUF — AI-conn fork

This is a fork, not our model. The abliterated weights are by huihui-ai; this GGUF conversion is by mradermacher; the base model is Qwen/Qwen3-Coder-30B-A3B-Instruct. This fork exists so the fleet's copy cannot be altered or pulled upstream, and to carry the fit notes below. License: Apache-2.0 (per the source repo).

Uncensored coder pick: MoE, 30B total / ~3B active per token — but note that all expert weights must sit in VRAM for GPU residency; "3B active" does not shrink the memory bill.

Recommended file for a 20 GB card: Huihui-Qwen3-Coder-30B-A3B-Instruct-abliterated.i1-Q4_K_M.gguf (17.28 GiB).

Ollama pull:

ollama pull hf.co/AI-conn/Huihui-Qwen3-Coder-30B-A3B-Instruct-abliterated-i1-GGUF:i1-Q4_K_M

Prime fit (measured budget, RX 7900 XT — 19.98 GiB VRAM, 32 GB RAM)

i1-Q4_K_M 17.28 GiB + small MoE KV + ~0.7 GiB reserve ≈ 18.3 GiB — resident, tight

i1-IQ4_XS (15.24 GiB) is the roomy option if you want more context headroom at slightly lower quant fidelity.

Independent datapoint (community harness): the stock base scores 60.4% on HumanEval Pro. No independent coding eval of any abliterated build exists yet — our fleet settles coder rankings with a local bake-off, not cards.