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 / UPSTREAM_SOURCE.md

Andrewsab

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

45b4eb7 verified 30 days ago

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	# Voice Scribe Model Mirror

	This repository is a Voice Scribe distribution mirror. The model artifacts are
	copied from the upstream repository and the source revision below is pinned.

	\| Field \| Value \|
	\| --- \| --- \|
	\| Layout key \| `corrector_source` \|
	\| Target directory in installer \| `qwen3-4b-instruct-2507-src` \|
	\| Upstream repo \| `Qwen/Qwen3-4B-Instruct-2507` \|
	\| Upstream revision \| `HEAD` \|
	\| Upstream resolved SHA \| `cdbee75f17c01a7cc42f958dc650907174af0554` \|
	\| Mirror created \| `2026-04-23T22:28:49Z` \|
	\| Description \| Qwen3-4B Instruct 2507 original HF source checkpoint. \|
	\| License metadata \| `{"license": "apache-2.0", "license_files": ["LICENSE"], "license_tags": ["license:apache-2.0"]}` \|

	## Installer Contract

	This mirror corresponds to `parakeet/installer/wrapper/model_catalog.py`.
	Required files for installer validation:

	```json
	[
	"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"
	]
	```

	Allowed installer subset patterns:

	```json
	[]
	```

	## Redistribution Note

	Do not make this repository public unless the upstream license and model card
	allow redistribution for the intended use. Private mirrors are for operational
	distribution convenience and reproducible installs.