GLM-4.7-Flash-Uncensored-Heretic-NEO-CODE-Imatrix-MAX-GGUF
GLM-4.7-Flash-Uncensored-Heretic-NEO-CODE-Imatrix-MAX-GGUF is a 30B mixture-of-experts model with ~2B active parameters, quantized for CPU/GPU inference. It is a community-modified version of the original GLM-4.7-Flash with content-filter modifications ('heretic'ed') and custom quantization tuning. The model is optimized for creative writing, storytelling, and reasoning tasks. It requires careful prompt engineering to achieve desired output quality and runs best with specific sampler settings (smoothing_factor, temperature, top-p). Gated: false; MIT license.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | DavidAU |
| Parameters | Unknown |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 41.7k |
| Likes | 393 |
| Last updated | 2026-06-12 |
| Source | DavidAU/GLM-4.7-Flash-Uncensored-Heretic-NEO-CODE-Imatrix-MAX-GGUF |
What GLM-4.7-Flash-Uncensored-Heretic-NEO-CODE-Imatrix-MAX-GGUF is
30B MOE architecture with 4 active experts (~2B parameters active); GGUF quantization variants (IQ4_NL, Q5_1, Q4_1, Q8_0 specialized; standard quants also available). Dual Imatrix optimization (NEO + Code datasets) and 16-bit precision output tensors. Context length: up to 200k (non-roped). Generated with LLAMACPP commit 7789 (Jan 21 2026). Known issues: Flash Attention offloaded to CPU causing low token generation speed; requires llama_HF config for text-generation-webui. Model is content-filter modified; baseline model: https://huggingface.co/zai-org/GLM-4.7-Flash.
Run GLM-4.7-Flash-Uncensored-Heretic-NEO-CODE-Imatrix-MAX-GGUF locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="DavidAU/GLM-4.7-Flash-Uncensored-Heretic-NEO-CODE-Imatrix-MAX-GGUF")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
Estimated VRAM: 8–16 GB for full-precision on GPU; 4–8 GB for quantized GGUF variants (Q4_1, Q5_1, IQ4_NL) depending on batch size and context length. CPU-only inference feasible (~2B active parameters) with slower token generation. Context handling up to 200k requires sufficient RAM/VRAM and batch-size constraints. Verify with your target quantization variant and inference engine (llama.cpp, KoboldCpp).
Not stated in card. Model is described as 'fine tune' capable (listed in tags), but no LoRA/QLoRA compatibility, training methodology, or fine-tuning guidance provided. Fine-tuning on custom GGUF quantized variants is not standard practice; would require conversion to full precision or source-format weights (referenced at https://huggingface.co/collections/DavidAU/d-au-source-files-for-gguf-exl2-awq-gptq-hqq-etc-etc-66b55cb8ba25f914cbf210be). Requires review for your use case.
When to avoid it — and what to weigh
- High-Speed Production Chat/Inference — Flash Attention disabled due to CPU offload issues and slow token generation. Not suitable for latency-sensitive applications or high-throughput serving without inference optimization.
- Factual Accuracy or Guardrailed Content Required — Model is content-filter modified ('uncensored'). Not designed for production systems requiring strict refusal guardrails, guardrailed safety boundaries, or factual consistency guarantees. Requires additional validation layers.
- Out-of-Box Safe Commercial Deployment — Specialized 'uncensored' variant; will generate explicit, profane, or unfiltered content with minimal prompt direction. Organizations with strict content policies should use the non-uncensored GLM-4.7-Flash variant instead.
- Immediate Production Use Without Testing — Card notes quant quality depends on LLAMACPP commit 7789+; older commits perform poorly. Requires validation on your target inference framework (llama.cpp, text-generation-webui, KoboldCpp, Silly Tavern) and careful sampler tuning before deployment.
License & commercial use
MIT license. Permissive OSI-approved license permits commercial use, modification, and distribution. No gating or access restrictions. Modifications (content-filter removal) are permitted under MIT.
MIT is a permissive OSI license and clearly allows commercial use. However, this model is a community modification ('uncensored/heretic'ed') of the base GLM-4.7-Flash (developed by zai-org). Commercial deployments should: (1) verify zai-org's original GLM-4.7-Flash license and commercial terms; (2) accept that content-filter removal may violate organizational policy or customer expectations in regulated industries; (3) ensure content governance and liability frameworks are in place for unfiltered model outputs. Recommend legal/compliance review for regulated industries.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | High |
| DEV.co fit | Good |
| Assessment confidence | Medium |
Content-filter removal increases risk of generating harmful, explicit, or unfiltered content in uncontrolled deployments. Model will generate profane, x-rated, or violent content with minimal prompt direction. No stated security audits, adversarial robustness testing, or content safety evaluation. Self-hosted deployment removes cloud-vendor security features (logging, DDoS protection, etc.). Inference via llama.cpp or KoboldCpp on exposed networks may leak model outputs or weights. Recommend: air-gapped deployment, input sanitization, output filtering for regulated use cases, and audit logging.
Alternatives to consider
GLM-4.7-Flash (zai-org, official)
Original, non-'uncensored' version with refusal guardrails intact. Use if you need safer content filtering, vendor-backed stability, or production guardrails. Same MOE architecture; no community modification risks.
Qwen2.5-32B or Llama-3.1-70B (open quantized variants)
Larger instruction-tuned alternatives with broader reasoning and code-gen capabilities. Standard quantization support, mature inference stacks, and wider adoption. Better for production systems requiring stable, vetted pipelines.
Mistral-7B or Mixtral-8x7B
Smaller, faster alternatives for creative/reasoning tasks with lower hardware overhead. Strong community support, comprehensive documentation, and production-grade serving infrastructure (vLLM, TGI). Trade-off: fewer 'thinking' tokens and slightly lower quality on complex reasoning.
Ship GLM-4.7-Flash-Uncensored-Heretic-NEO-CODE-Imatrix-MAX-GGUF with senior software developers
This model excels at creative writing and reasoning but requires careful sampler tuning and governance frameworks for unfiltered content. Assess hardware, inference framework compatibility (llama.cpp, KoboldCpp, Silly Tavern), and content policy fit before production. Review our advanced deployment guide or request a custom LLM evaluation.
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GLM-4.7-Flash-Uncensored-Heretic-NEO-CODE-Imatrix-MAX-GGUF FAQ
Can I use this model commercially?
What GPU/VRAM do I need?
Why is token generation slow?
How do I get the best output quality?
Custom software development services
From first prototype to production, DEV.co delivers software development services around tools like GLM-4.7-Flash-Uncensored-Heretic-NEO-CODE-Imatrix-MAX-GGUF. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across open-source llms and beyond.
Deploy GLM-4.7-Flash-Uncensored Safely
This model excels at creative writing and reasoning but requires careful sampler tuning and governance frameworks for unfiltered content. Assess hardware, inference framework compatibility (llama.cpp, KoboldCpp, Silly Tavern), and content policy fit before production. Review our advanced deployment guide or request a custom LLM evaluation.