Instructions to use tangledgroup/tangled-alpha-0.12-core with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tangledgroup/tangled-alpha-0.12-core with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tangledgroup/tangled-alpha-0.12-core")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tangledgroup/tangled-alpha-0.12-core", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use tangledgroup/tangled-alpha-0.12-core with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tangledgroup/tangled-alpha-0.12-core" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tangledgroup/tangled-alpha-0.12-core", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/tangledgroup/tangled-alpha-0.12-core
- SGLang
How to use tangledgroup/tangled-alpha-0.12-core 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 "tangledgroup/tangled-alpha-0.12-core" \ --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": "tangledgroup/tangled-alpha-0.12-core", "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 "tangledgroup/tangled-alpha-0.12-core" \ --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": "tangledgroup/tangled-alpha-0.12-core", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use tangledgroup/tangled-alpha-0.12-core with Docker Model Runner:
docker model run hf.co/tangledgroup/tangled-alpha-0.12-core
Download scripts/requirements.in from tangledgroup/tangled-alpha-0.12-core: direct link, hf CLI and curl.
- Browser
- Download file 471 Bytes
-
https://huggingface.co/tangledgroup/tangled-alpha-0.12-core/resolve/2f59073e799a71b34a721857c2f4a4210bb2b907/scripts/requirements.in
- Command line
-
hf download hf://tangledgroup/tangled-alpha-0.12-core@2f59073e799a71b34a721857c2f4a4210bb2b907/scripts/requirements.in
-
curl -L -o requirements.in https://huggingface.co/tangledgroup/tangled-alpha-0.12-core/resolve/2f59073e799a71b34a721857c2f4a4210bb2b907/scripts/requirements.in
471 Bytes
| # pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu | |
| torch>=2.5.0,<2.6.0 | |
| numpy<2.0 | |
| tqdm | |
| Pillow | |
| datasets | |
| jinja2 | |
| transformers | |
| wandb | |
| litdata==0.2.17 | |
| litgpt[all] @ git+https://github.com/Lightning-AI/litgpt.git | |
| # mergekit @ git+https://github.com/arcee-ai/mergekit.git | |
| # torchao | |
| # bitsandbytes | |
| # grokadamw | |
| sophia-opt | |
| # dolphinflow @ git+https://github.com/cognitivecomputations/dolphinflow-optimizer.git | |
| # unsloth | |
| lm_eval[ifeval,math] | |