Instructions to use tzervas/Qwen2.5-1.5B-Instruct-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tzervas/Qwen2.5-1.5B-Instruct-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="tzervas/Qwen2.5-1.5B-Instruct-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tzervas/Qwen2.5-1.5B-Instruct-AWQ")
model = AutoModelForCausalLM.from_pretrained("tzervas/Qwen2.5-1.5B-Instruct-AWQ", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tzervas/Qwen2.5-1.5B-Instruct-AWQ with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/tzervas/Qwen2.5-1.5B-Instruct-AWQ

SGLang

How to use tzervas/Qwen2.5-1.5B-Instruct-AWQ 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 "tzervas/Qwen2.5-1.5B-Instruct-AWQ" \
    --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": "tzervas/Qwen2.5-1.5B-Instruct-AWQ",
		"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 "tzervas/Qwen2.5-1.5B-Instruct-AWQ" \
        --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": "tzervas/Qwen2.5-1.5B-Instruct-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tzervas/Qwen2.5-1.5B-Instruct-AWQ with Docker Model Runner:
```
docker model run hf.co/tzervas/Qwen2.5-1.5B-Instruct-AWQ
```

Configuration Parsing Warning:In config.json: "quantization_config.modules_to_not_convert" must be an array

AutoAWQ INT4 (w_bit=4, q_group_size=128, GEMM) of Qwen2.5-1.5B-Instruct, run on akula-prime 3090 Ti after gpu-pack free of qwen3.5-9b. vLLM restored.

Not BitNet. Not GGUF Q4. Pascal 1080ti stays BitNet I2_S. Hot 3090 production remains QuantTrio/Qwen3.5-9B-AWQ. This 1.5B AWQ is the warm-queue / HF-library card-fit for smaller Ampere jobs.

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Safetensors

Model size

2B params

Tensor type

I32

BF16

F16

Model tree for tzervas/Qwen2.5-1.5B-Instruct-AWQ

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Qwen/Qwen2.5-1.5B

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Qwen/Qwen2.5-1.5B-Instruct

Quantized

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