Instructions to use almanach/manta-lm-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use almanach/manta-lm-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="almanach/manta-lm-small", trust_remote_code=True)# Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("almanach/manta-lm-small", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use almanach/manta-lm-small with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "almanach/manta-lm-small" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "almanach/manta-lm-small", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/almanach/manta-lm-small
- SGLang
How to use almanach/manta-lm-small 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 "almanach/manta-lm-small" \ --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": "almanach/manta-lm-small", "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 "almanach/manta-lm-small" \ --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": "almanach/manta-lm-small", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use almanach/manta-lm-small with Docker Model Runner:
docker model run hf.co/almanach/manta-lm-small
Download pytorch_model.bin from almanach/manta-lm-small: direct link, hf CLI and curl.
- Browser
- Download file 228 MB
-
https://huggingface.co/almanach/manta-lm-small/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://almanach/manta-lm-small/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/almanach/manta-lm-small/resolve/main/pytorch_model.bin
228 MB
- Xet hash:
- 26dc87cafa8eec8e5ff9bb8bdae75a4c64bc96ef1329bc8771addad929a8dad2
- Size of remote file:
- 228 MB
- SHA256:
- 6b4083be46225ef58fcc4016ceb1f20385e3e52ec69b31270a78f4e817a0cd43
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