Instructions to use KalyanJyothula18/Qwen3-8B-Lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KalyanJyothula18/Qwen3-8B-Lite with Transformers:

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

pipe = pipeline("text-generation", model="KalyanJyothula18/Qwen3-8B-Lite")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KalyanJyothula18/Qwen3-8B-Lite")
model = AutoModelForCausalLM.from_pretrained("KalyanJyothula18/Qwen3-8B-Lite", 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 KalyanJyothula18/Qwen3-8B-Lite with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KalyanJyothula18/Qwen3-8B-Lite"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KalyanJyothula18/Qwen3-8B-Lite",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KalyanJyothula18/Qwen3-8B-Lite

SGLang

How to use KalyanJyothula18/Qwen3-8B-Lite 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 "KalyanJyothula18/Qwen3-8B-Lite" \
    --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": "KalyanJyothula18/Qwen3-8B-Lite",
		"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 "KalyanJyothula18/Qwen3-8B-Lite" \
        --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": "KalyanJyothula18/Qwen3-8B-Lite",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use KalyanJyothula18/Qwen3-8B-Lite with Docker Model Runner:
```
docker model run hf.co/KalyanJyothula18/Qwen3-8B-Lite
```

Qwen3 Highlights

Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:

Uniquely support of seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue) within single model, ensuring optimal performance across various scenarios. Significantly enhancement in its reasoning capabilities, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning. Superior human preference alignment, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience. Expertise in agent capabilities, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks. Support of 100+ languages and dialects with strong capabilities for multilingual instruction following and translation.

Model Details

Qwen3-8B-Lite is a lightweight variant of the Qwen3 series of generative AI models with approximately 8 billion parameters designed for efficient language understanding and generation tasks. It is optimized for faster inference and lower compute requirements while maintaining competitive accuracy.

Model type: Transformer-based generative language model (e.g., decoder-only or encoder-decoder)
Language(s): Primarily English (or specify other supported languages)
Finetuned from: Base Qwen3-8B

Model Description

Model Type: Causal Language Model (decoder-only transformer)
Training Stage: Pretraining and Post-training quantization (FP8)
Number of Parameters: Approximately 8.2 billion total parameters
Number of Parameters (Non-Embedding): Approximately 6.95 billion
Number of Layers: 36 transformer layers
Number of Attention Heads (Grouped Query Attention - GQA): 32 heads for Query 8 heads for Key-Value

Direct Use

Text completion, generation, and summarization
Chatbots and conversational AI
Language understanding tasks like classification and question answering

Downstream Use [optional]

Fine-tuning on domain-specific data
Integration into NLP pipelines and applications

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Safetensors

Model size

8B params

Tensor type

F16

F32