Text Generation
Transformers
Safetensors
llama
slmkit
from-scratch
abc-music
music
abc-notation
folk-music
symbolic-music
text-generation-inference
Instructions to use m87techlabs/abc-folk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use m87techlabs/abc-folk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="m87techlabs/abc-folk")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("m87techlabs/abc-folk") model = AutoModelForCausalLM.from_pretrained("m87techlabs/abc-folk", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use m87techlabs/abc-folk with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "m87techlabs/abc-folk" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "m87techlabs/abc-folk", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/m87techlabs/abc-folk
- SGLang
How to use m87techlabs/abc-folk 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 "m87techlabs/abc-folk" \ --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": "m87techlabs/abc-folk", "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 "m87techlabs/abc-folk" \ --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": "m87techlabs/abc-folk", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use m87techlabs/abc-folk with Docker Model Runner:
docker model run hf.co/m87techlabs/abc-folk
Download tokenizer.json from m87techlabs/abc-folk: direct link, hf CLI and curl.
- Browser
- Download file 2.07 kB
-
https://huggingface.co/m87techlabs/abc-folk/resolve/main/tokenizer.json
- Command line
-
hf download hf://m87techlabs/abc-folk/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/m87techlabs/abc-folk/resolve/main/tokenizer.json
2.07 kB
| { | |
| "version": "1.0", | |
| "truncation": null, | |
| "padding": null, | |
| "added_tokens": [ | |
| { | |
| "id": 0, | |
| "content": "<unk>", | |
| "single_word": false, | |
| "lstrip": false, | |
| "rstrip": false, | |
| "normalized": false, | |
| "special": true | |
| }, | |
| { | |
| "id": 1, | |
| "content": "<eos>", | |
| "single_word": false, | |
| "lstrip": false, | |
| "rstrip": false, | |
| "normalized": false, | |
| "special": true | |
| } | |
| ], | |
| "normalizer": null, | |
| "pre_tokenizer": { | |
| "type": "Split", | |
| "pattern": { | |
| "Regex": "[\\s\\S]" | |
| }, | |
| "behavior": "Isolated", | |
| "invert": false | |
| }, | |
| "post_processor": null, | |
| "decoder": { | |
| "type": "Fuse" | |
| }, | |
| "model": { | |
| "type": "WordLevel", | |
| "vocab": { | |
| "<unk>": 0, | |
| "<eos>": 1, | |
| "\t": 2, | |
| "\n": 3, | |
| " ": 4, | |
| "!": 5, | |
| "#": 6, | |
| "%": 7, | |
| "'": 8, | |
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| ")": 10, | |
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| ",": 13, | |
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| ".": 15, | |
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| "<": 28, | |
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| ">": 30, | |
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| "[": 52, | |
| "\\": 53, | |
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| "_": 56, | |
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| "a": 58, | |
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| "z": 82, | |
| "{": 83, | |
| "|": 84, | |
| "}": 85, | |
| "~": 86 | |
| }, | |
| "unk_token": "<unk>" | |
| } | |
| } |