Instructions to use VoiceScribe/qwen3-5-0.8b-dictation-corrector-cuda-int4-nf4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VoiceScribe/qwen3-5-0.8b-dictation-corrector-cuda-int4-nf4 with Transformers:

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

pipe = pipeline("text-generation", model="VoiceScribe/qwen3-5-0.8b-dictation-corrector-cuda-int4-nf4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("VoiceScribe/qwen3-5-0.8b-dictation-corrector-cuda-int4-nf4")
model = AutoModelForCausalLM.from_pretrained("VoiceScribe/qwen3-5-0.8b-dictation-corrector-cuda-int4-nf4", device_map="auto")
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 Settings

vLLM

How to use VoiceScribe/qwen3-5-0.8b-dictation-corrector-cuda-int4-nf4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VoiceScribe/qwen3-5-0.8b-dictation-corrector-cuda-int4-nf4"
# 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/qwen3-5-0.8b-dictation-corrector-cuda-int4-nf4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/VoiceScribe/qwen3-5-0.8b-dictation-corrector-cuda-int4-nf4

SGLang

How to use VoiceScribe/qwen3-5-0.8b-dictation-corrector-cuda-int4-nf4 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/qwen3-5-0.8b-dictation-corrector-cuda-int4-nf4" \
    --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/qwen3-5-0.8b-dictation-corrector-cuda-int4-nf4",
		"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/qwen3-5-0.8b-dictation-corrector-cuda-int4-nf4" \
        --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/qwen3-5-0.8b-dictation-corrector-cuda-int4-nf4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use VoiceScribe/qwen3-5-0.8b-dictation-corrector-cuda-int4-nf4 with Docker Model Runner:
```
docker model run hf.co/VoiceScribe/qwen3-5-0.8b-dictation-corrector-cuda-int4-nf4
```

Voice Scribe Russian Dictation Corrector (Qwen3.5-0.8B, V15 R-3, cuda-int4-nf4)

Consumer ship-form: bitsandbytes 4-bit NF4 (double-quant). ~750 MB. -12pp wild vs bf16 (still passes consumer gate >=78%); preserves hard-neg 5/5 absolute. Target: RTX 2060+/6GB.

Eval results (held-out wild_eval, 58 prompts × 9 sectors)

Metric	Score
Wild pass	84.48%
Hard-negative	5/5
Smoke	8/8
p50 latency	676 ms
Ship-form size	750 MB

Comparison:

macOS V15 R-3 reference: 93.1% wild
V14 baseline: 86.2%
Qwen3-4B Q5 production (pre-LoRA): 48%
This model: 84.48% (-1.7pp vs V14 baseline)

Training recipe (V15 R-3)

Mirrors macOS configs/r4_v15_extended.yaml byte-for-byte logical-recipe.

base               = Qwen/Qwen3.5-0.8B (vanilla, NOT Instruct)
LoRA rank          = 16
LoRA alpha         = 80 (rsLoRA mode -> effective scale 20.0)
target_modules     = q_proj, k_proj, v_proj, o_proj
layers_to_transform = last 16 of 24 (range(8, 24))
mask_prompt        = ON (assistant_masks via patched chat_template {% generation %})
max_steps          = 1100
batch_size         = 2
max_seq_length     = 384
lr_schedule        = cosine, peak 3e-5, warmup 100
weight_decay       = 0.01
optim              = adamw_torch_fused
precision          = bf16
seed               = 20260515
trainable params   = 720,896 (0.0957% of 753M)
data               = 1104 rows = V14 seeds (691) + V15 brand expansion (271) + V15 R-3 patches (142)

Intended use

Russian dictation cleanup after ASR (GigaAM, Whisper, Parakeet)
Conservative editing policy: remove filler (эм/ну/типа/короче), normalize Cyrillic IT terms (гитхаб -> GitHub), preserve all meaning
NOT for general text editing, English text, creative writing, summarization, translation

Limitations

58-row eval set has ±1.72pp single-row noise
Cyrillic <-> Latin choice on ambiguous brand spellings is judgment call (model may differ from expected byte-match)
Trained on synthetic data only; real production telemetry collection planned for V16
INT4 NF4 has higher quality loss (-12pp) than AWQ would; AWQ blocked on Win-Py3.13-cu128 (no source build without nvcc)

Hardware ship matrix

Hardware	Recommended ship-form	This model?
RTX 5090 / 4090 24GB+	bf16
RTX 4070 / 4060 / 3060 8-16GB	INT8
RTX 2060 / 3050 / 4060 6-8GB	INT4 NF4	PRIMARY
Re-training / stacking	adapter

Inference

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "VoiceScribe/qwen3-5-0.8b-dictation-corrector-cuda-int4-nf4",
    load_in_4bit=True,
    device_map="cuda",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("VoiceScribe/qwen3-5-0.8b-dictation-corrector-cuda-int4-nf4", trust_remote_code=True)

messages = [
    {"role": "system", "content": "Корректор русской диктовки. Убери слова-паразиты ..."},
    {"role": "user", "content": "Запушил коммит в гитхаб репозиторий"},
]
prompt = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=False,
    enable_thinking=False,  # CRITICAL for Qwen3.5
)
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
out = model.generate(**inputs, max_new_tokens=200, do_sample=False)
print(tokenizer.decode(out[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))
# Expected: "Запушил коммит в GitHub репозиторий"

Cross-platform variants

macOS MLX: VoiceScribe/qwen3-5-0.8b-dictation-corrector-mlx-{bf16,8bit,4bit} (V15 R-3, 93.1% wild)
CUDA bf16/INT8/INT4-NF4: this family (V15 R-3 Win port, 84.48-96.55% wild)
OpenVINO: planned (separate venv for export; tracker WP#920)
TensorRT-RTX W4A16: deferred (DeltaNet ONNX export blocked on Win-Py3.13-cu128 in 2026-05)

Citation

@software{voicescribe-corrector-v15r3-2026,
  title = {Voice Scribe Russian Dictation Corrector (Qwen3.5-0.8B V15 R-3, CUDA Win port)},
  author = {Sabynin, Andrey},
  year = {2026},
  url = {https://huggingface.co/VoiceScribe/qwen3-5-0.8b-dictation-corrector-cuda-int4-nf4}
}

Trackers

macOS R&D: OpenProject WP#917 (V14), WP#919 (V15 R-3 macOS)
Windows port: OpenProject WP#920 (this effort, achieved 96.55% vs macOS 93.1%)

Downloads last month: 14

Safetensors

Model size

0.8B params

Tensor type

F32

BF16

Model tree for VoiceScribe/qwen3-5-0.8b-dictation-corrector-cuda-int4-nf4

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Adapter

(198)

this model