Text Generation
Transformers
Safetensors
llama
symbolic-music
music-generation
musicxml
midi
remi
bar-major
text-generation-inference
Instructions to use haster/Fermata-0.2B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use haster/Fermata-0.2B-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="haster/Fermata-0.2B-v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("haster/Fermata-0.2B-v1") model = AutoModelForCausalLM.from_pretrained("haster/Fermata-0.2B-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use haster/Fermata-0.2B-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "haster/Fermata-0.2B-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "haster/Fermata-0.2B-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/haster/Fermata-0.2B-v1
- SGLang
How to use haster/Fermata-0.2B-v1 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 "haster/Fermata-0.2B-v1" \ --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": "haster/Fermata-0.2B-v1", "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 "haster/Fermata-0.2B-v1" \ --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": "haster/Fermata-0.2B-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use haster/Fermata-0.2B-v1 with Docker Model Runner:
docker model run hf.co/haster/Fermata-0.2B-v1
Commit History
18 days ago
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Rename to Fermata-0.2B-v1; not related to Cloudflare Clef; known issues
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list conditioning vocabulary (genres, ensembles, key/tempo/meter ranges)
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add prompt examples and samples section
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add piano concerto sample
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add rock sample
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default sampling: temp 0.7 / top_k 24
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Clef-0.2B: safetensors weights, tokenizer, model card
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initial commit
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