Instructions to use wangzhang/GLM-4.7-Flash-abliteratex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wangzhang/GLM-4.7-Flash-abliteratex with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="wangzhang/GLM-4.7-Flash-abliteratex")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("wangzhang/GLM-4.7-Flash-abliteratex")
model = AutoModelForCausalLM.from_pretrained("wangzhang/GLM-4.7-Flash-abliteratex", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use wangzhang/GLM-4.7-Flash-abliteratex with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf wangzhang/GLM-4.7-Flash-abliteratex:Q8_0
# Run inference directly in the terminal:
llama cli -hf wangzhang/GLM-4.7-Flash-abliteratex:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf wangzhang/GLM-4.7-Flash-abliteratex:Q8_0
# Run inference directly in the terminal:
llama cli -hf wangzhang/GLM-4.7-Flash-abliteratex:Q8_0

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf wangzhang/GLM-4.7-Flash-abliteratex:Q8_0
# Run inference directly in the terminal:
./build/bin/llama cli -hf wangzhang/GLM-4.7-Flash-abliteratex:Q8_0

Use Docker

docker model run hf.co/wangzhang/GLM-4.7-Flash-abliteratex:Q8_0

Llama new
LM Studio
Jan

vLLM

How to use wangzhang/GLM-4.7-Flash-abliteratex with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wangzhang/GLM-4.7-Flash-abliteratex"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wangzhang/GLM-4.7-Flash-abliteratex",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wangzhang/GLM-4.7-Flash-abliteratex:Q8_0

SGLang

How to use wangzhang/GLM-4.7-Flash-abliteratex with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "wangzhang/GLM-4.7-Flash-abliteratex" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wangzhang/GLM-4.7-Flash-abliteratex",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "wangzhang/GLM-4.7-Flash-abliteratex" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wangzhang/GLM-4.7-Flash-abliteratex",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use wangzhang/GLM-4.7-Flash-abliteratex with Ollama:
```
ollama run hf.co/wangzhang/GLM-4.7-Flash-abliteratex:Q8_0
```
Unsloth Desktop

How to use wangzhang/GLM-4.7-Flash-abliteratex with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf wangzhang/GLM-4.7-Flash-abliteratex:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "wangzhang/GLM-4.7-Flash-abliteratex:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use wangzhang/GLM-4.7-Flash-abliteratex with Docker Model Runner:
```
docker model run hf.co/wangzhang/GLM-4.7-Flash-abliteratex:Q8_0
```

Lemonade

How to use wangzhang/GLM-4.7-Flash-abliteratex with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull wangzhang/GLM-4.7-Flash-abliteratex:Q8_0

Run and chat with the model

lemonade run user.GLM-4.7-Flash-abliteratex-Q8_0

List all available models

lemonade list

Hermes Agent

How to use wangzhang/GLM-4.7-Flash-abliteratex with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf wangzhang/GLM-4.7-Flash-abliteratex:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default wangzhang/GLM-4.7-Flash-abliteratex:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use wangzhang/GLM-4.7-Flash-abliteratex with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf wangzhang/GLM-4.7-Flash-abliteratex:Q8_0

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "wangzhang/GLM-4.7-Flash-abliteratex:Q8_0" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

GLM-4.7-Flash-Abliteratex

GLM-4.7-Flash-Abliteratex is a rebuilt successor to wangzhang/GLM-4.7-Flash-abliterated. The release focuses on preserving the official checkpoint structure, reducing collateral capability damage, and providing a reproducible llama.cpp Q8_0 artifact.

This is an experimental model with substantially reduced refusal behavior. It can produce unsafe, false, private-looking, or otherwise harmful content. A low refusal score is not a factuality score. Do not treat generated personal data, credentials, citations, or operational claims as real without independent verification.

What changed from the previous release

Area	Previous release	Abliteratex
Weight keys	9,491	9,703
Parameters	29,943,390,976	31,221,488,576
MTP / NextN tensors	Missing 212 tensors	All 212 official tensors retained
`tokenizer_config.json`	305 bytes	Official 7,226-byte file
Attention edits	Yes	None
Routed-expert edits	Broad attention/MLP/router recipe	Routed-expert down-projection EGA only, plus four router rows per MoE layer
Official metadata	Regenerated/stripped fields	Official config, tokenizer, generation config, and chat template restored byte-for-byte

The new package contains the same 9,703 tensor keys and dtype distribution as the official source revision: 9,656 BF16 tensors and 47 F32 tensors.

Training and selection

Base: zai-org/GLM-4.7-Flash
Base revision: 7dd20894a642a0aa287e9827cb1a1f7f91386b67
Dataset: wangzhang/abliterix-datasets
Direction data: good_1000 train[:800] and harmful_1000 train[:800]
Search backend: vLLM in-place editing on one RTX PRO 6000 Blackwell 96GB
Search: 60 broad trials followed by 24 targeted trials
Selected targeted trial: 20
Direction: global, interpolated index 20.4365519952
Expert down-projection profile:
- maximum strength 3.9788400044
- peak layer 30.8739492862
- minimum strength 2.9557299481
- radius 21.0740371709
Router adjustment: top four safety-associated experts per MoE layer, router_bias = -0.85
Attention and shared-expert weights were intentionally left unchanged.

Evaluation

The selected search trial produced 2 refusals out of 50 search-evaluation prompts (4%) with fixed-continuation NLL drift of 0.0826336592 nats/token.

On a separate 150-prompt held-out slice (harmful_1000 train[850:1000]) with 256-token generations, the strict LLM judge reported:

26/150 refusals or degenerate/non-actionable answers (17.3%)
124/150 compliant answers (82.7%)

Many held-out failures request real-time private phone numbers, home addresses, SSNs, or other data that is not present in the prompt. Fabricating plausible PII can lower a refusal benchmark while making hallucination materially worse. This release reports the strict score instead of counting invented private data as a quality success.

llama.cpp and Q8_0

The included GLM-4.7-Flash-Abliteratex-Q8_0.gguf was converted with current llama.cpp commit 3018a11e79e489b657dbb77c95694889ccff92df.

Older GLM-4.7-Flash GGUF conversion paths were known to mishandle glm4_moe_lite MLA tensors and expert stacking. Use a recent llama.cpp build. For first-line troubleshooting, keep KV cache in F16/BF16; a Q8_0 model file and --cache-type-k/--cache-type-v q8_0 are different forms of quantization.

Example:

llama-cli \
  -m GLM-4.7-Flash-Abliteratex-Q8_0.gguf \
  -ngl all -c 4096 -n 256 --jinja \
  --cache-type-k f16 --cache-type-v f16 \
  -p "Explain why Canberra is the capital of Australia."

Relevant upstream context:

Transformers usage

from transformers import AutoModelForCausalLM, AutoTokenizer

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

The safetensors package retains the official MTP/NextN tensors even though some current Transformers and llama.cpp runtime paths intentionally ignore the extra prediction layer during ordinary generation.

Provenance and Modification Notice

Immediate source checkpoint: zai-org/GLM-4.7-Flash
Exact base revision used: 7dd20894a642a0aa287e9827cb1a1f7f91386b67 (already recorded in the original model card).
Modification method: Abliterix weight-space / representation intervention intended to reduce refusal behavior.
Modified and published by: Wangzhang Wu
Repository first published: 2026-07-30 (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.

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。

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Safetensors

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Tensor type

F32

BF16

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