Instructions to use breadlicker45/MusePy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use breadlicker45/MusePy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="breadlicker45/MusePy")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("breadlicker45/MusePy") model = AutoModelForMultimodalLM.from_pretrained("breadlicker45/MusePy") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use breadlicker45/MusePy with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "breadlicker45/MusePy" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "breadlicker45/MusePy", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/breadlicker45/MusePy
- SGLang
How to use breadlicker45/MusePy 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 "breadlicker45/MusePy" \ --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": "breadlicker45/MusePy", "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 "breadlicker45/MusePy" \ --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": "breadlicker45/MusePy", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use breadlicker45/MusePy with Docker Model Runner:
docker model run hf.co/breadlicker45/MusePy
Commit ·
f34c08a
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Parent(s): dc09d65
Upload 4 files
Browse files- config.json +17 -46
- tokenizer.json +0 -0
- tokenizer_config.json +1 -1
config.json
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"_name_or_path": "
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"attention_types": [
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"bos_token_id": 50256,
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"embed_dropout": 0,
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"eos_token_id": 50256,
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"gradient_checkpointing": false,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size":
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"line_by_line": true,
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"max_position_embeddings": 2048,
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"summary_proj_to_labels": true,
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"summary_type": "cls_index",
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"summary_use_proj": true,
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"torch_dtype": "float32",
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"transformers_version": "4.
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"use_cache": true,
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"_name_or_path": "EleutherAI/pythia-19m",
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"architectures": [
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"GPTNeoXForCausalLM"
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"bos_token_id": 0,
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"eos_token_id": 0,
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"hidden_act": "gelu",
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"hidden_size": 512,
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"initializer_range": 0.02,
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"intermediate_size": 2048,
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"layer_norm_eps": 1e-05,
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"line_by_line": true,
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"max_position_embeddings": 2048,
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"model_type": "gpt_neox",
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"num_attention_heads": 8,
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"num_hidden_layers": 6,
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"rotary_emb_base": 10000,
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"rotary_pct": 0.25,
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"tie_word_embeddings": false,
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"torch_dtype": "float32",
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"transformers_version": "4.25.1",
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"use_cache": true,
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"use_parallel_residual": true,
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"vocab_size": 50304
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}
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tokenizer.json
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tokenizer_config.json
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"name_or_path": "EleutherAI/
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"eos_token": "<|endoftext|>",
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"tokenizer_class": "PreTrainedTokenizerFast"
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"name_or_path": "EleutherAI/polyglot-ko-1.3b",
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"eos_token": "<|endoftext|>",
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"pad_token": "<|endoftext|>",
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"tokenizer_class": "PreTrainedTokenizerFast"
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