Instructions to use BertilBraun/qwen3-1.7b-voice-light-tool-use-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BertilBraun/qwen3-1.7b-voice-light-tool-use-merged with Transformers:

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

pipe = pipeline("text-generation", model="BertilBraun/qwen3-1.7b-voice-light-tool-use-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BertilBraun/qwen3-1.7b-voice-light-tool-use-merged")
model = AutoModelForCausalLM.from_pretrained("BertilBraun/qwen3-1.7b-voice-light-tool-use-merged")
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 BertilBraun/qwen3-1.7b-voice-light-tool-use-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BertilBraun/qwen3-1.7b-voice-light-tool-use-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BertilBraun/qwen3-1.7b-voice-light-tool-use-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BertilBraun/qwen3-1.7b-voice-light-tool-use-merged

SGLang

How to use BertilBraun/qwen3-1.7b-voice-light-tool-use-merged 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 "BertilBraun/qwen3-1.7b-voice-light-tool-use-merged" \
    --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": "BertilBraun/qwen3-1.7b-voice-light-tool-use-merged",
		"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 "BertilBraun/qwen3-1.7b-voice-light-tool-use-merged" \
        --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": "BertilBraun/qwen3-1.7b-voice-light-tool-use-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BertilBraun/qwen3-1.7b-voice-light-tool-use-merged with Docker Model Runner:
```
docker model run hf.co/BertilBraun/qwen3-1.7b-voice-light-tool-use-merged
```

Voice Light Qwen3 1.7B Tool-Use - Merged

This repository is the standalone BF16 inference checkpoint for the Voice Light conversational model. It merges the Voice Light tool-use LoRA into the original Qwen3-1.7B weights. It does not require PEFT or dynamic LoRA loading at inference time.

Sources

Base model: Qwen/Qwen3-1.7B at revision 70d244cc86ccca08cf5af4e1e306ecf908b1ad5e
LoRA adapter: BertilBraun/qwen3-1.7b-voice-light-tool-use-lora at revision 5c88c42fe092159e960126823223c3f46056cd7d
Training dataset: BertilBraun/voice-light-tool-use-synthetic

Refer to those repositories for the original model documentation, adapter training information, licenses, and intended-use constraints.

The dataset includes the exact teacher-led v22 generator snapshot, locked environment, tests, teacher-server command, end-to-end reproduction launcher, immutable teacher revision, generation manifests, deterministic scenario seeds, and file hashes under reproducibility/source-8c32f70.

Merge

Method: PEFT merge_and_unload(safe_merge=True)
Merge device: CPU
Weight format: safetensors with a 5GB maximum shard size
Weight dtype: BF16
Hugging Face Hub: 1.18.0
Safetensors: 0.8.0
Transformers: 5.14.1
PEFT: 0.18.1
PyTorch: 2.11.0+cu130

The exact machine-readable source revisions and merge settings are included in merge-provenance.json. The destination repository expected when this artifact was built is BertilBraun/qwen3-1.7b-voice-light-tool-use-merged.

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Safetensors

Model size

2B params

Tensor type

BF16

Model tree for BertilBraun/qwen3-1.7b-voice-light-tool-use-merged

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

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BertilBraun
/

qwen3-1.7b-voice-light-tool-use-merged

Voice Light Qwen3 1.7B Tool-Use - Merged

Sources

Merge

Model tree for BertilBraun/qwen3-1.7b-voice-light-tool-use-merged

Dataset used to train BertilBraun/qwen3-1.7b-voice-light-tool-use-merged