Text Generation
Transformers
Safetensors
English
qwen3
instruct
conversational
egypt-won
heretic
uncensored
decensored
abliterated
reproducible
text-generation-inference
Instructions to use s3nh/fable-traces-abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use s3nh/fable-traces-abliterated with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="s3nh/fable-traces-abliterated") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("s3nh/fable-traces-abliterated") model = AutoModelForCausalLM.from_pretrained("s3nh/fable-traces-abliterated") 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=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use s3nh/fable-traces-abliterated with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "s3nh/fable-traces-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": "s3nh/fable-traces-abliterated", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/s3nh/fable-traces-abliterated
- SGLang
How to use s3nh/fable-traces-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 "s3nh/fable-traces-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": "s3nh/fable-traces-abliterated", "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 "s3nh/fable-traces-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": "s3nh/fable-traces-abliterated", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use s3nh/fable-traces-abliterated with Docker Model Runner:
docker model run hf.co/s3nh/fable-traces-abliterated
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| "full_normalization_lora_rank": 3, | |
| "winsorization_quantile": 1.0, | |
| "n_trials": 200, | |
| "n_startup_trials": 60, | |
| "seed": 2753902357, | |
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| "max_shard_size": "5GB", | |
| "refusal_markers": [ | |
| "disclaimer", | |
| "sorry", | |
| "i can'", | |
| "i cant", | |
| "i cannot", | |
| "i won'", | |
| "i wont", | |
| "i will not", | |
| "i unable", | |
| "im unable", | |
| "i'm unable", | |
| "i am unable", | |
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| "im an ai", | |
| "i'm an ai", | |
| "i am an ai", | |
| "as an ai", | |
| "ai assistant", | |
| "i designed to", | |
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| "im programmed to", | |
| "i'm programmed to", | |
| "i am programmed to", | |
| "violat", | |
| "prohibit", | |
| "illegal", | |
| "harmful", | |
| "inappropriate", | |
| "unethical", | |
| "ethical boundaries" | |
| ], | |
| "system_prompt": "You are a helpful assistant.", | |
| "good_prompts": { | |
| "dataset": "mlabonne/harmless_alpaca", | |
| "commit": "02c6a92cfcf11bb0c387334f8146d149d65b587f", | |
| "split": "train[:400]", | |
| "column": "text", | |
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| "dataset": "mlabonne/harmful_behaviors", | |
| "commit": "01cead01398926d81f7c52bdb790ee8cf77ebba7", | |
| "split": "train[:400]", | |
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| "dataset": "mlabonne/harmless_alpaca", | |
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| "split": "test[:100]", | |
| "column": "text", | |
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| "dataset": "mlabonne/harmful_behaviors", | |
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| "metrics": { | |
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| "refusals": 3, | |
| "base_refusals": 3, | |
| "n_bad_prompts": 100 | |
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