GLM-4.7-Flash-abliterated

Unrestricted version of zai-org/GLM-4.7-Flash, created using Abliterix.

Model Details

Property	Value
Base Model	zai-org/GLM-4.7-Flash
Architecture	GLM-4 MoE Lite with Multi-head Latent Attention (MLA)
Parameters	30B total / 3B active per token
Layers	47 (1 dense + 46 MoE)
Experts	64 routed + 1 shared, top-4 routing
Hidden Size	2048
Context Length	128K tokens
Precision	BF16

Performance

Metric	This model	Original
KL divergence	0.0133	0
Refusals	1/100 (1%)	92/100 (92%)

Evaluated with an LLM judge (Gemini Flash) on 100 harmful prompts. KL divergence of 0.0133 indicates the model's general capabilities are virtually identical to the original.

How It Was Made

1. Computed refusal directions from 400 harmful vs 400 benign prompt pairs across all 47 layers
2. Applied orthogonalized abliteration to isolate refusal-specific activation patterns
3. Steered two component types independently: attention output projections (MLA) and MLP/expert down-projections (including shared experts)
4. Profiled MoE expert activations across 46 router layers to identify safety-critical experts
5. Applied hybrid MoE steering: router weight suppression (21 experts, bias=-1.64) + fused expert abliteration (weight=1.85)
6. Optimized via Optuna TPE over 50 trials (15 warmup), selected trial #48

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "wangzhang/GLM-4.7-Flash-abliterated",
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
    "wangzhang/GLM-4.7-Flash-abliterated",
    trust_remote_code=True,
)

messages = [{"role": "user", "content": "Your question here"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Hardware Requirements

Precision	VRAM
BF16	~56 GB (A100 80GB, H100)
INT8	~30 GB (A40, RTX 4090)
NF4	~15 GB (RTX 3090, RTX 4080)

Disclaimer

This model is intended for research purposes only. The removal of safety guardrails means the model will comply with requests that the original model would refuse. Users are responsible for ensuring their use complies with applicable laws and regulations.

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Made with Abliterix

Provenance and Modification Notice

• Immediate source checkpoint: zai-org/GLM-4.7-Flash
• Exact base revision used: Not recorded in the existing release artifacts; the current upstream HEAD is not substituted.
• Modification method: Abliterix weight-space / representation intervention intended to reduce refusal behavior.
• Modified and published by: Wangzhang Wu
• Repository first published: 2026-02-26 (Hugging Face repository metadata)

The original model weights and/or derived checkpoint were modified. This repository is an independent derivative and is not an official release of the upstream model developer.

License and Attribution

The governing upstream license is MIT License. A copy is included in LICENSE. License source audited on 2026-08-29: https://huggingface.co/zai-org/GLM-4.7-Flash

All applicable upstream copyright, attribution, acceptable-use, and other license terms remain in effect. This repository grants no rights beyond those provided by the upstream license. Downstream users must preserve applicable license and attribution notices.

License evidence note: The upstream model card declares MIT, while the related official GLM code repository declares Apache-2.0. This repository follows the model-specific Hugging Face metadata pending written upstream clarification.

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Disclaimer and Responsible Use / 免责声明与安全使用声明

English

This is an experimental, modified model provided for research, evaluation, and other lawful purposes. Its safety alignment, refusal behavior, or other safeguards may have been weakened or removed. It may produce inaccurate, biased, offensive, explicit, dangerous, or illegal content. Outputs are not professional advice and must not be relied on for medical, legal, financial, safety-critical, or other high-stakes decisions without qualified human review.

You are solely responsible for how you access, use, deploy, fine-tune, or redistribute this model and its outputs, including compliance with applicable laws, regulations, licenses, third-party rights, platform policies, and the original model's terms. Do not use it to facilitate harm, illegal activity, malware, fraud, privacy violations, targeted harassment, weapons development, or decisions that materially affect a person's rights or access to essential services without appropriate authorization, safeguards, and qualified oversight.

Before deployment, perform a context-specific risk assessment and testing; use human oversight, access controls, content filtering, rate limits, monitoring, logging, and incident-response procedures as appropriate. Preserve this notice in downstream redistributions.

The model is provided "AS IS", without warranties of any kind. To the fullest extent permitted by applicable law, the maintainer disclaims liability for claims, damages, or losses arising from use, misuse, inability to use, or redistribution of the model or its outputs. Nothing in this notice overrides applicable law or the governing license, and this notice is not legal advice.

中文

本模型属于实验性改造模型，仅供研究、评测及其他合法用途。其安全对齐、拒答机制或其他防护可能已被削弱或移除，因此可能生成不准确、偏见、冒犯、露骨、危险或违法内容。输出不构成医疗、法律、金融等专业意见；涉及高风险或重大权益的决定，必须由具备资质的人员复核。

使用者须对模型及其输出的访问、使用、部署、微调和再分发承担全部责任，并遵守适用法律法规、许可证、第三方权利、平台政策及原模型条款。不得将本模型用于促成伤害、违法活动、恶意软件、欺诈、侵犯隐私、定向骚扰、武器开发，或在缺乏适当授权、防护和专业监督时，用于实质影响个人权利或基本服务获取的决策。

部署前应进行与具体场景相匹配的风险评估和测试，并酌情采用人工监督、访问控制、内容过滤、限流、监控、日志和事件响应措施；下游再分发时应保留本声明。

本模型按“现状”提供，不附带任何形式的保证。在适用法律允许的最大范围内，维护者不对因使用、误用、无法使用或再分发本模型及其输出而产生的索赔、损害或损失承担责任。本声明不取代适用法律或管辖本模型的许可证，也不构成法律意见。