Text Generation
Transformers
English
bismuth
causal-lm
hyperbolic-geometry
cdm
crystalformer
novel-architecture
duoneural
custom_code
Instructions to use DuoNeural/bismuth-104m-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DuoNeural/bismuth-104m-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DuoNeural/bismuth-104m-base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("DuoNeural/bismuth-104m-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use DuoNeural/bismuth-104m-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DuoNeural/bismuth-104m-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DuoNeural/bismuth-104m-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/DuoNeural/bismuth-104m-base
- SGLang
How to use DuoNeural/bismuth-104m-base 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 "DuoNeural/bismuth-104m-base" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DuoNeural/bismuth-104m-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "DuoNeural/bismuth-104m-base" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DuoNeural/bismuth-104m-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use DuoNeural/bismuth-104m-base with Docker Model Runner:
docker model run hf.co/DuoNeural/bismuth-104m-base
Commit History
5 days ago
-
Add Paper 41 DOI + cross-link to hyperbolic-peft; fix paper count 40->37
Verified signature708c794 -
update modeling_bismuth.py (oct14 audit revisions)
Verified signature649e407 -
update configuration_bismuth.py (oct14 audit revisions)
Verified signaturecc20c91 -
update config.json (oct14 audit revisions)
Verified signature5c707a1 -
update README.md (oct14 audit revisions)
Verified signature6c9ea30
6 days ago
-
Upload README.md with huggingface_hub
Verified signature17c4ed1 -
Upload README.md with huggingface_hub
Verified signatureccc21c9 -
Upload train_bismuth_125m.py with huggingface_hub
Verified signatureaece6c2 -
Upload hyp_cdm_transformer.py with huggingface_hub
Verified signaturec3d5e0c -
Upload bismuth_125m_telemetry.jsonl with huggingface_hub
Verified signature98f0372 -
Upload bismuth_125m_step_78000.pt with huggingface_hub
Verified signature949603f -
initial commit
Verified signature9ab5d78