Instructions to use kaitchup/Qwen2.5-1.5B-Instruct-AutoRound-GPTQ-asym-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kaitchup/Qwen2.5-1.5B-Instruct-AutoRound-GPTQ-asym-4bit with Transformers:

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

pipe = pipeline("text-generation", model="kaitchup/Qwen2.5-1.5B-Instruct-AutoRound-GPTQ-asym-4bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kaitchup/Qwen2.5-1.5B-Instruct-AutoRound-GPTQ-asym-4bit")
model = AutoModelForCausalLM.from_pretrained("kaitchup/Qwen2.5-1.5B-Instruct-AutoRound-GPTQ-asym-4bit", 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 kaitchup/Qwen2.5-1.5B-Instruct-AutoRound-GPTQ-asym-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kaitchup/Qwen2.5-1.5B-Instruct-AutoRound-GPTQ-asym-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kaitchup/Qwen2.5-1.5B-Instruct-AutoRound-GPTQ-asym-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kaitchup/Qwen2.5-1.5B-Instruct-AutoRound-GPTQ-asym-4bit

SGLang

How to use kaitchup/Qwen2.5-1.5B-Instruct-AutoRound-GPTQ-asym-4bit 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 "kaitchup/Qwen2.5-1.5B-Instruct-AutoRound-GPTQ-asym-4bit" \
    --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": "kaitchup/Qwen2.5-1.5B-Instruct-AutoRound-GPTQ-asym-4bit",
		"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 "kaitchup/Qwen2.5-1.5B-Instruct-AutoRound-GPTQ-asym-4bit" \
        --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": "kaitchup/Qwen2.5-1.5B-Instruct-AutoRound-GPTQ-asym-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kaitchup/Qwen2.5-1.5B-Instruct-AutoRound-GPTQ-asym-4bit with Docker Model Runner:
```
docker model run hf.co/kaitchup/Qwen2.5-1.5B-Instruct-AutoRound-GPTQ-asym-4bit
```

Qwen2.5-1.5B-Instruct-AutoRound-GPTQ-asym-4bit

File size: 1,290 Bytes

37cbd11

{
  "_name_or_path": "Qwen/Qwen2.5-1.5B-Instruct",
  "architectures": [
    "Qwen2ForCausalLM"
  ],
  "attention_dropout": 0.0,
  "bos_token_id": 151643,
  "eos_token_id": 151645,
  "hidden_act": "silu",
  "hidden_size": 1536,
  "initializer_range": 0.02,
  "intermediate_size": 8960,
  "max_position_embeddings": 32768,
  "max_window_layers": 21,
  "model_type": "qwen2",
  "num_attention_heads": 12,
  "num_hidden_layers": 28,
  "num_key_value_heads": 2,
  "quantization_config": {
    "amp": true,
    "autoround_version": "0.3",
    "bits": 4,
    "damp_percent": 0.01,
    "data_type": "int",
    "desc_act": false,
    "enable_minmax_tuning": true,
    "enable_quanted_input": true,
    "gradient_accumulate_steps": 1,
    "group_size": 128,
    "iters": 1000,
    "low_gpu_mem_usage": false,
    "lr": 0.001,
    "minmax_lr": 0.001,
    "nsamples": 512,
    "quant_method": "gptq",
    "scale_dtype": "torch.float16",
    "seqlen": 2048,
    "sym": false,
    "train_bs": 8,
    "true_sequential": false
  },
  "rms_norm_eps": 1e-06,
  "rope_scaling": null,
  "rope_theta": 1000000.0,
  "sliding_window": null,
  "tie_word_embeddings": true,
  "torch_dtype": "float16",
  "transformers_version": "4.45.2",
  "use_cache": true,
  "use_sliding_window": false,
  "vocab_size": 151936
}