Instructions to use xun/Qwen-Audio-Chat-Int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xun/Qwen-Audio-Chat-Int4 with Transformers:

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

pipe = pipeline("text-generation", model="xun/Qwen-Audio-Chat-Int4", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("xun/Qwen-Audio-Chat-Int4", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use xun/Qwen-Audio-Chat-Int4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xun/Qwen-Audio-Chat-Int4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xun/Qwen-Audio-Chat-Int4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/xun/Qwen-Audio-Chat-Int4

SGLang

How to use xun/Qwen-Audio-Chat-Int4 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 "xun/Qwen-Audio-Chat-Int4" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xun/Qwen-Audio-Chat-Int4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "xun/Qwen-Audio-Chat-Int4" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xun/Qwen-Audio-Chat-Int4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use xun/Qwen-Audio-Chat-Int4 with Docker Model Runner:
```
docker model run hf.co/xun/Qwen-Audio-Chat-Int4
```

Qwen-Audio-Chat-Int4 / config.json

xun

init

8bf83e9 almost 3 years ago

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1.43 kB

	{
	"architectures": [
	"QWenLMHeadModel"
	],
	"auto_map": {
	"AutoConfig": "configuration_qwen.QWenConfig",
	"AutoModelForCausalLM": "modeling_qwen.QWenLMHeadModel"
	},
	"audio": {
	"add_audio_bos_eos_token": true,
	"audio_start_id": 155163,
	"avg_pool": true,
	"n_ctx": 1500,
	"n_head": 20,
	"n_layer": 32,
	"n_mels": 80,
	"n_state": 1280,
	"output_dim": 4096
	},
	"attn_dropout_prob": 0.0,
	"bf16": false,
	"emb_dropout_prob": 0.0,
	"fp16": true,
	"fp32": false,
	"hidden_size": 4096,
	"intermediate_size": 22016,
	"initializer_range": 0.02,
	"kv_channels": 128,
	"layer_norm_epsilon": 1e-06,
	"max_position_embeddings": 8192,
	"model_type": "qwen",
	"no_bias": true,
	"num_attention_heads": 32,
	"num_hidden_layers": 32,
	"onnx_safe": null,
	"quantization_config": {
	"bits": 4,
	"group_size": 128,
	"damp_percent": 0.01,
	"desc_act": false,
	"static_groups": false,
	"sym": true,
	"true_sequential": true,
	"model_name_or_path": null,
	"model_file_base_name": "model",
	"quant_method": "gptq"
	},
	"rotary_emb_base": 10000,
	"rotary_pct": 1.0,
	"scale_attn_weights": true,
	"seq_length": 8192,
	"tie_word_embeddings": false,
	"tokenizer_class": "QWenTokenizer",
	"transformers_version": "4.32.0",
	"use_cache": true,
	"use_dynamic_ntk": true,
	"use_flash_attn": "auto",
	"use_logn_attn": true,
	"vocab_size": 155947
	}