Instructions to use VoiceScribe/voicescribe-corrector-source with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VoiceScribe/voicescribe-corrector-source with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="VoiceScribe/voicescribe-corrector-source")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("VoiceScribe/voicescribe-corrector-source")
model = AutoModelForCausalLM.from_pretrained("VoiceScribe/voicescribe-corrector-source")
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-source with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VoiceScribe/voicescribe-corrector-source"
# 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-source",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/VoiceScribe/voicescribe-corrector-source

SGLang

How to use VoiceScribe/voicescribe-corrector-source 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-source" \
    --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-source",
		"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-source" \
        --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-source",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use VoiceScribe/voicescribe-corrector-source with Docker Model Runner:
```
docker model run hf.co/VoiceScribe/voicescribe-corrector-source
```

voicescribe-corrector-source / voicescribe-model-layout.json

Andrewsab

Voice Scribe mirror corrector_source from Qwen/Qwen3-4B-Instruct-2507@cdbee75f17c0

45b4eb7 verified about 1 month ago

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858 Bytes

	{
	"schema_version": 1,
	"generated_at": "2026-04-23T22:28:49Z",
	"layout_key": "corrector_source",
	"target_dir": "qwen3-4b-instruct-2507-src",
	"upstream_repo": "Qwen/Qwen3-4B-Instruct-2507",
	"upstream_revision": "",
	"upstream_sha": "cdbee75f17c01a7cc42f958dc650907174af0554",
	"description": "Qwen3-4B Instruct 2507 original HF source checkpoint.",
	"required_files": [
	"config.json",
	"generation_config.json",
	"tokenizer.json",
	"tokenizer_config.json",
	"model.safetensors.index.json",
	"model-00001-of-00003.safetensors",
	"model-00002-of-00003.safetensors",
	"model-00003-of-00003.safetensors"
	],
	"allow_patterns": [],
	"license_metadata": {
	"license": "apache-2.0",
	"license_tags": [
	"license:apache-2.0"
	],
	"license_files": [
	"LICENSE"
	]
	}
	}