Instructions to use prithivMLmods/Tureis-Qwen3_QWQ-4B-Exp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Tureis-Qwen3_QWQ-4B-Exp with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/Tureis-Qwen3_QWQ-4B-Exp")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Tureis-Qwen3_QWQ-4B-Exp")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Tureis-Qwen3_QWQ-4B-Exp")
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

vLLM

How to use prithivMLmods/Tureis-Qwen3_QWQ-4B-Exp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Tureis-Qwen3_QWQ-4B-Exp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Tureis-Qwen3_QWQ-4B-Exp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Tureis-Qwen3_QWQ-4B-Exp

SGLang

How to use prithivMLmods/Tureis-Qwen3_QWQ-4B-Exp 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 "prithivMLmods/Tureis-Qwen3_QWQ-4B-Exp" \
    --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": "prithivMLmods/Tureis-Qwen3_QWQ-4B-Exp",
		"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 "prithivMLmods/Tureis-Qwen3_QWQ-4B-Exp" \
        --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": "prithivMLmods/Tureis-Qwen3_QWQ-4B-Exp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Tureis-Qwen3_QWQ-4B-Exp with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Tureis-Qwen3_QWQ-4B-Exp
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Tureis-Qwen3_QWQ-4B-Exp

Tureis-Qwen3_QWQ-4B-Exp is a fine-tuned variant of the Qwen3-4B architecture, trained specifically on QWQ Synthetic datasets to maximize precise mathematical and logical reasoning. This experimental model offers high accuracy on structured reasoning tasks while maintaining lightweight performance, making it ideal for technical, educational, and symbolic computation applications.

GGUF : https://huggingface.co/prithivMLmods/Tureis-Qwen3_QWQ-4B-Exp-Q4_K_S-GGUF

Key Features

Precision Reasoning with QWQ Dataset Tailored for high-fidelity symbolic reasoning, step-by-step math problem solving, and logic tasks, thanks to specialized QWQ synthetic fine-tuning.
Lightweight Code Understanding Capable of interpreting, generating, and correcting code in Python, C++, and other languages, optimized for concise logic-based tasks.
Structured Output Formatting Generates well-organized responses in Markdown, JSON, LaTeX, and tabular formats suitable for notebooks, documentation, and data-centric workflows.
Instruction-Following Accuracy Tuned to follow multi-step user instructions with consistency across tasks and sessions, improving reliability in educational and factual domains.
Multilingual Capabilities Supports reasoning and generation in more than 20 languages for global accessibility and technical translation use cases.
Efficient 4B Architecture Based on Qwen3-4B, providing an optimal tradeoff between performance and compute requirements—suitable for mid-tier GPUs or scaled inference scenarios.

Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Tureis-Qwen3_QWQ-4B-Exp"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "If 5(x - 2) = 3x + 4, solve for x step-by-step."

messages = [
    {"role": "system", "content": "You are a precise reasoning assistant trained on QWQ datasets."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)