Instructions to use mayank64ce/Newq-0.5B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mayank64ce/Newq-0.5B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="mayank64ce/Newq-0.5B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mayank64ce/Newq-0.5B-Instruct")
model = AutoModelForCausalLM.from_pretrained("mayank64ce/Newq-0.5B-Instruct", 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 mayank64ce/Newq-0.5B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mayank64ce/Newq-0.5B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mayank64ce/Newq-0.5B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mayank64ce/Newq-0.5B-Instruct

SGLang

How to use mayank64ce/Newq-0.5B-Instruct 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 "mayank64ce/Newq-0.5B-Instruct" \
    --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": "mayank64ce/Newq-0.5B-Instruct",
		"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 "mayank64ce/Newq-0.5B-Instruct" \
        --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": "mayank64ce/Newq-0.5B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mayank64ce/Newq-0.5B-Instruct with Docker Model Runner:
```
docker model run hf.co/mayank64ce/Newq-0.5B-Instruct
```

Newq-0.5B-Instruct

Newq is a compact 0.5B-parameter instruction-tuned language model for chat and general text generation. It is small enough to run comfortably on CPU or a modest GPU, and supports long contexts.

Details

Parameters: 0.49B (0.36B non-embedding)
Layers: 24
Attention heads: 14 for Q, 2 for KV (GQA)
Context length: 32,768 tokens
Precision: bfloat16

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "mayank64ce/Newq-0.5B-Instruct"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)

messages = [{"role": "user", "content": "Give me a short introduction to large language models."}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)

out = model.generate(**inputs, max_new_tokens=512)
out = out[0][len(inputs.input_ids[0]):]
print(tokenizer.decode(out, skip_special_tokens=True))

License and attribution

Released under the Apache License 2.0.

This model is a derivative of Qwen/Qwen2.5-0.5B-Instruct by the Qwen team, Alibaba Cloud, used under Apache-2.0. The original license text is included in this repository.

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Safetensors

Model size

0.5B params

Tensor type

BF16

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