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

Libraries

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
```

voicescribe-corrector / openvino_detokenizer.xml

Andrewsab

Voice Scribe mirror corrector from anubhav200/Qwen3-4B-Instruct-2507-openvino-int4@ea34b26c6e5f

4ef73dc verified about 1 month ago

raw

history blame contribute delete

10.1 kB

	<?xml version="1.0"?>
	<net name="detokenizer" version="11">
	<layers>
	<layer id="0" name="Parameter_122293" type="Parameter" version="opset1">
	<data shape="?,?" element_type="i64" />
	<output>
	<port id="0" precision="I64" names="Parameter_122293">
	<dim>-1</dim>
	<dim>-1</dim>
	</port>
	</output>
	</layer>
	<layer id="1" name="Convert_122492" type="Convert" version="opset1">
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	<input>
	<port id="0" precision="I64">
	<dim>-1</dim>
	<dim>-1</dim>
	</port>
	</input>
	<output>
	<port id="1" precision="I32">
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	</port>
	</output>
	</layer>
	<layer id="2" name="Constant_122295" type="Const" version="opset1">
	<data element_type="i32" shape="151669" offset="0" size="606676" />
	<output>
	<port id="0" precision="I32">
	<dim>151669</dim>
	</port>
	</output>
	</layer>
	<layer id="3" name="Constant_122297" type="Const" version="opset1">
	<data element_type="i32" shape="151669" offset="606676" size="606676" />
	<output>
	<port id="0" precision="I32">
	<dim>151669</dim>
	</port>
	</output>
	</layer>
	<layer id="4" name="Constant_122299" type="Const" version="opset1">
	<data element_type="u8" shape="976309" offset="1213352" size="976309" />
	<output>
	<port id="0" precision="U8">
	<dim>976309</dim>
	</port>
	</output>
	</layer>
	<layer id="5" name="Slice_122304" type="Const" version="opset1">
	<data element_type="i32" shape="14" offset="2189661" size="56" />
	<output>
	<port id="0" precision="I32">
	<dim>14</dim>
	</port>
	</output>
	</layer>
	<layer id="6" name="VocabDecoder_122306" type="VocabDecoder" version="extension">
	<data skip_tokens="" />
	<input>
	<port id="0" precision="I32">
	<dim>-1</dim>
	<dim>-1</dim>
	</port>
	<port id="1" precision="I32">
	<dim>151669</dim>
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	<port id="2" precision="I32">
	<dim>151669</dim>
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	<port id="3" precision="U8">
	<dim>976309</dim>
	</port>
	<port id="4" precision="I32">
	<dim>14</dim>
	</port>
	</input>
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	<port id="5" precision="I32">
	<dim>-1</dim>
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	<port id="8" precision="I32">
	<dim>-1</dim>
	</port>
	<port id="9" precision="U8">
	<dim>-1</dim>
	</port>
	</output>
	</layer>
	<layer id="7" name="FuzeRagged_122307" type="FuzeRagged" version="extension">
	<input>
	<port id="0" precision="I32">
	<dim>-1</dim>
	</port>
	<port id="1" precision="I32">
	<dim>-1</dim>
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	<dim>-1</dim>
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	<port id="3" precision="I32">
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	</input>
	<output>
	<port id="4" precision="I32">
	<dim>-1</dim>
	</port>
	<port id="5" precision="I32">
	<dim>-1</dim>
	</port>
	</output>
	</layer>
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	<data replace_mode="true" />
	<input>
	<port id="0" precision="I32">
	<dim>-1</dim>
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	<port id="1" precision="I32">
	<dim>-1</dim>
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	<dim>-1</dim>
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	<port id="5" precision="U8">
	<dim>-1</dim>
	</port>
	</output>
	</layer>
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	<input>
	<port id="0" precision="I32">
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	<port id="1" precision="I32">
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	</port>
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	</input>
	<output>
	<port id="3" precision="STRING" names="Result_122310,string_output">
	<dim>-1</dim>
	</port>
	</output>
	</layer>
	<layer id="10" name="Result_122310" type="Result" version="opset1" output_names="Result_122310,string_output">
	<input>
	<port id="0" precision="STRING">
	<dim>-1</dim>
	</port>
	</input>
	</layer>
	</layers>
	<edges>
	<edge from-layer="0" from-port="0" to-layer="1" to-port="0" />
	<edge from-layer="1" from-port="1" to-layer="6" to-port="0" />
	<edge from-layer="2" from-port="0" to-layer="6" to-port="1" />
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	</edges>
	<rt_info>
	<add_attention_mask value="True" />
	<add_prefix_space />
	<add_special_tokens value="True" />
	<chat_template value="{%- if tools %} {{- '<\|im_start\|>system\n' }} {%- if messages[0].role == 'system' %} {{- messages[0].content + '\n\n' }} {%- endif %} {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }} {%- for tool in tools %} {{- "\n" }} {{- tool \| tojson }} {%- endfor %} {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><\|im_end\|>\n" }} {%- else %} {%- if messages[0].role == 'system' %} {{- '<\|im_start\|>system\n' + messages[0].content + '<\|im_end\|>\n' }} {%- endif %} {%- endif %} {%- for message in messages %} {%- if message.content is string %} {%- set content = message.content %} {%- else %} {%- set content = '' %} {%- endif %} {%- if (message.role == "user") or (message.role == "system" and not loop.first) %} {{- '<\|im_start\|>' + message.role + '\n' + content + '<\|im_end\|>' + '\n' }} {%- elif message.role == "assistant" %} {{- '<\|im_start\|>' + message.role + '\n' + content }} {%- if message.tool_calls %} {%- for tool_call in message.tool_calls %} {%- if (loop.first and content) or (not loop.first) %} {{- '\n' }} {%- endif %} {%- if tool_call.function %} {%- set tool_call = tool_call.function %} {%- endif %} {{- '<tool_call>\n{"name": "' }} {{- tool_call.name }} {{- '", "arguments": ' }} {%- if tool_call.arguments is string %} {{- tool_call.arguments }} {%- else %} {{- tool_call.arguments \| tojson }} {%- endif %} {{- '}\n</tool_call>' }} {%- endfor %} {%- endif %} {{- '<\|im_end\|>\n' }} {%- elif message.role == "tool" %} {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} {{- '<\|im_start\|>user' }} {%- endif %} {{- '\n<tool_response>\n' }} {{- content }} {{- '\n</tool_response>' }} {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} {{- '<\|im_end\|>\n' }} {%- endif %} {%- endif %} {%- endfor %} {%- if add_generation_prompt %} {{- '<\|im_start\|>assistant\n' }} {%- endif %}" />
	<clean_up_tokenization_spaces />
	<detokenizer_input_type value="i64" />
	<eos_token_id value="151645" />
	<handle_special_tokens_with_re />
	<max_length />
	<number_of_inputs value="1" />
	<openvino_tokenizers_version value="2026.0.0.0-632-47cea02a2d4" />
	<openvino_version value="2026.0.0-20897-967e454238b" />
	<original_post_processor_template value="{"type": "ByteLevel", "add_prefix_space": false, "trim_offsets": false, "use_regex": false}" />
	<original_tokenizer_class value="<class 'transformers.models.qwen2.tokenization_qwen2_fast.Qwen2TokenizerFast'>" />
	<pad_token_id value="151643" />
	<processed_post_processor_template value="{"single": {"ids": [-1], "type_ids": [0]}, "pair": {"ids": [-1, -2], "type_ids": [0, 0]}}" />
	<sentencepiece_version value="0.2.1" />
	<skip_special_tokens value="True" />
	<streaming_detokenizer value="False" />
	<tokenizer_output_type value="i64" />
	<tokenizers_version value="0.21.4" />
	<transformers_version value="4.53.0" />
	<use_max_padding value="False" />
	<use_sentencepiece_backend value="False" />
	<utf8_replace_mode value="replace" />
	<with_detokenizer value="True" />
	</rt_info>
	</net>