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
| absl-py==2.5.0 | |
| accelerate==1.14.0 | |
| alembic==1.18.5 | |
| annotated-doc==0.0.4 | |
| annotated-types==0.7.0 | |
| anyio==4.14.1 | |
| bitsandbytes==0.49.2 | |
| certifi==2026.6.17 | |
| chardet==6.0.0.post1 | |
| charset-normalizer==3.4.9 | |
| click==8.4.2 | |
| colorama==0.4.6 | |
| colorlog==6.10.1 | |
| cuda-bindings==13.3.1 | |
| cuda-pathfinder==1.5.6 | |
| cuda-toolkit==13.0.3.0 | |
| dataproperty==1.1.1 | |
| datasets==4.8.5 | |
| defusedxml==0.7.1 | |
| dill==0.4.1 | |
| evaluate==0.4.6 | |
| filelock==3.29.7 | |
| fsspec==2026.2.0 | |
| greenlet==3.5.3 | |
| h11==0.16.0 | |
| heretic-llm==1.4.0 | |
| hf-xet==1.5.1 | |
| httpcore==1.0.9 | |
| httpx==0.28.1 | |
| huggingface-hub==1.23.0 | |
| idna==3.18 | |
| immutabledict==4.3.1 | |
| jinja2==3.1.6 | |
| joblib==1.5.3 | |
| langdetect==1.0.9 | |
| lm-eval==0.4.12 | |
| lxml==6.1.1 | |
| mako==1.3.12 | |
| markdown-it-py==4.2.0 | |
| markupsafe==3.0.3 | |
| mbstrdecoder==1.1.5 | |
| mdurl==0.1.2 | |
| more-itertools==11.1.0 | |
| mpmath==1.3.0 | |
| multiprocess==0.70.19 | |
| narwhals==2.23.0 | |
| networkx==3.6.1 | |
| nltk==3.10.0 | |
| numpy==2.5.1 | |
| nvidia-cublas==13.1.1.3 | |
| nvidia-cuda-nvrtc==13.0.88 | |
| nvidia-cudnn-cu13==9.20.0.48 | |
| nvidia-cusparselt-cu13==0.8.1 | |
| nvidia-nccl-cu13==2.29.7 | |
| nvidia-nvshmem-cu13==3.4.5 | |
| optuna==4.9.0 | |
| packaging==26.2 | |
| pandas==3.0.3 | |
| pathvalidate==3.3.1 | |
| peft==0.19.1 | |
| pillow==12.3.0 | |
| portalocker==3.2.0 | |
| prompt-toolkit==3.0.52 | |
| psutil==7.2.2 | |
| py-cpuinfo==9.0.0 | |
| pyarrow==25.0.0 | |
| pydantic==2.13.4 | |
| pydantic-core==2.46.4 | |
| pydantic-settings==2.14.2 | |
| pygments==2.20.0 | |
| pytablewriter==1.2.1 | |
| python-dateutil==2.9.0.post0 | |
| python-dotenv==1.2.2 | |
| pyyaml==6.0.3 | |
| questionary==2.1.1 | |
| regex==2026.6.28 | |
| requests==2.34.2 | |
| rich==14.3.4 | |
| rouge-score==0.1.2 | |
| sacrebleu==2.6.0 | |
| safetensors==0.8.0 | |
| scikit-learn==1.9.0 | |
| scipy==1.18.0 | |
| setuptools==83.0.0 | |
| shellingham==1.5.4 | |
| six==1.17.0 | |
| sqlalchemy==2.0.51 | |
| sqlitedict==2.1.0 | |
| sympy==1.14.0 | |
| tabledata==1.3.5 | |
| tabulate==0.10.0 | |
| tcolorpy==0.1.7 | |
| threadpoolctl==3.6.0 | |
| tokenizers==0.22.2 | |
| tomli-w==1.2.0 | |
| torch==2.13.0 | |
| torchvision==0.28.0 | |
| tqdm==4.68.4 | |
| transformers==5.13.0 | |
| triton==3.7.1 | |
| typepy==1.3.5 | |
| typer==0.26.8 | |
| typing-extensions==4.16.0 | |
| typing-inspection==0.4.2 | |
| tzdata==2026.3 | |
| urllib3==2.7.0 | |
| wcwidth==0.8.2 | |
| word2number==1.1 | |
| xxhash==3.8.1 | |