Instructions to use VoiceScribe/voicescribe-corrector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VoiceScribe/voicescribe-corrector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="VoiceScribe/voicescribe-corrector") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("VoiceScribe/voicescribe-corrector") model = AutoModelForCausalLM.from_pretrained("VoiceScribe/voicescribe-corrector") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use VoiceScribe/voicescribe-corrector with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "VoiceScribe/voicescribe-corrector" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "VoiceScribe/voicescribe-corrector", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/VoiceScribe/voicescribe-corrector
- SGLang
How to use VoiceScribe/voicescribe-corrector 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 "VoiceScribe/voicescribe-corrector" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "VoiceScribe/voicescribe-corrector", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "VoiceScribe/voicescribe-corrector" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "VoiceScribe/voicescribe-corrector", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use VoiceScribe/voicescribe-corrector with Docker Model Runner:
docker model run hf.co/VoiceScribe/voicescribe-corrector
File size: 1,137 Bytes
4ef73dc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | # File created with: OpenVINO Model Server 2025.4.0.8db21b81
input_stream: "HTTP_REQUEST_PAYLOAD:input"
output_stream: "HTTP_RESPONSE_PAYLOAD:output"
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input_side_packet: "LLM_NODE_RESOURCES:llm"
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node_options: {
[type.googleapis.com / mediapipe.LLMCalculatorOptions]: {
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device: "NPU",
models_path: "./",
plugin_config: '{"NPUW_LLM_ENABLE_PREFIX_CACHING":true}',
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input_stream_handler {
input_stream_handler: "SyncSetInputStreamHandler",
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