Instructions to use dudeman2512/Muse-Glimmer-30B-abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dudeman2512/Muse-Glimmer-30B-abliterated with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="dudeman2512/Muse-Glimmer-30B-abliterated") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("dudeman2512/Muse-Glimmer-30B-abliterated") model = AutoModelForMultimodalLM.from_pretrained("dudeman2512/Muse-Glimmer-30B-abliterated", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.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=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use dudeman2512/Muse-Glimmer-30B-abliterated with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dudeman2512/Muse-Glimmer-30B-abliterated" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dudeman2512/Muse-Glimmer-30B-abliterated", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/dudeman2512/Muse-Glimmer-30B-abliterated
- SGLang
How to use dudeman2512/Muse-Glimmer-30B-abliterated 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 "dudeman2512/Muse-Glimmer-30B-abliterated" \ --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": "dudeman2512/Muse-Glimmer-30B-abliterated", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "dudeman2512/Muse-Glimmer-30B-abliterated" \ --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": "dudeman2512/Muse-Glimmer-30B-abliterated", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use dudeman2512/Muse-Glimmer-30B-abliterated with Docker Model Runner:
docker model run hf.co/dudeman2512/Muse-Glimmer-30B-abliterated
Muse-Glimmer-30B-abliterated
Muse-Glimmer-30B with its refusal direction removed, produced with Heretic. BF16, 13 shards, 59.55 GB.
Abliteration identifies the direction in the model's residual stream that corresponds to refusing a request, and subtracts that component from the weights. It is a direct edit to the checkpoint, not fine-tuning: no gradient steps, no training data.
Result
| Refusals, original | 2348 / 7011 (33.49%) |
| Refusals, abliterated | 88 / 7011 (1.26%) |
| Refusals removed | 96.3% |
| KL divergence vs original | 0.2241 |
KL divergence measures how far the abliterated model's next-token distribution has moved from the original on prompts it would never have refused — the collateral cost of the edit. Heretic warns that values above ~0.5 usually indicate significant damage to the original model's capabilities; this is well under half that.
How this configuration was chosen
Heretic's abliteration is parameterised by how hard to cut, where in the layer stack the cut peaks, how far it spreads, and which layer the refusal direction is read from. Those interact, so the configuration was selected by search rather than by hand, in two stages:
- 120 trials were evaluated against a fixed 1,000-prompt subsample, three workers sharing one Optuna study. Cheap enough to explore the space.
- The 15 best candidates were then re-measured against the full 7,011 harmful / 12,000 harmless sets.
The second stage was not a formality. Refusal count is a rare-event count, so a 1,000-prompt estimate is noisy: candidates the first stage ranked 23 against 34 came back 261 against 259 when measured properly — a genuine inversion. KL, being a smooth statistic, reproduced almost exactly at both sample sizes.
The winning configuration removed 3× more refusals than the runner-up while doing less damage to the model (KL 0.224 against 0.334), so it is not simply the most aggressive setting available.
Parameters
| attn.o_proj | mlp.down_proj | |
|---|---|---|
| max_weight | 1.478300 | 1.411696 |
| max_weight_position | 37.196841 | 33.810336 |
| min_weight | 1.411191 | 1.407139 |
| min_weight_distance | 29.973070 | 25.019048 |
Direction scope global, direction index 36.589359.
Note that min_weight is nearly equal to max_weight in both components: the
winning cut is close to uniform across the layers it touches, rather than a
narrow peak.
Integrity
Verified before publication: 13 shards in the index, 13 on disk, no missing shards, no orphans, declared size matching the bytes on disk exactly, and every shard's safetensors header parsing with precisely the tensor names its index entry claims.
Use with vLLM
vllm serve dudeman2512/Muse-Glimmer-30B-abliterated
Caveat
This model has had its refusal behaviour removed. It will attempt requests the original declines. Whatever guardrails you need belong at the application layer.
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