Instructions to use prithivMLmods/Kepler-Qwen3-4B-Super-Thinking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Kepler-Qwen3-4B-Super-Thinking with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/Kepler-Qwen3-4B-Super-Thinking")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Kepler-Qwen3-4B-Super-Thinking")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Kepler-Qwen3-4B-Super-Thinking")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use prithivMLmods/Kepler-Qwen3-4B-Super-Thinking with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Kepler-Qwen3-4B-Super-Thinking"
# 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/Kepler-Qwen3-4B-Super-Thinking",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Kepler-Qwen3-4B-Super-Thinking

SGLang

How to use prithivMLmods/Kepler-Qwen3-4B-Super-Thinking 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/Kepler-Qwen3-4B-Super-Thinking" \
    --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/Kepler-Qwen3-4B-Super-Thinking",
		"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/Kepler-Qwen3-4B-Super-Thinking" \
        --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/Kepler-Qwen3-4B-Super-Thinking",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Kepler-Qwen3-4B-Super-Thinking with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Kepler-Qwen3-4B-Super-Thinking
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Kepler-Qwen3-4B-Super-Thinking

Kepler-Qwen3-4B-Super-Thinking is a reasoning-focused model fine-tuned on Qwen for Abliterated Reasoning and polished token probabilities, enhancing balanced multilingual generation across mathematics and general-purpose reasoning. It specializes in event-driven logic, structured analysis, and precise probabilistic modeling—making it an ideal tool for researchers, educators, and developers working with uncertainty and structured reasoning.

GGUF: https://huggingface.co/prithivMLmods/Kepler-Qwen3-4B-Super-Thinking-GGUF

Key Features

Abliterated Reasoning Enhanced reasoning precision through polished token probability distributions in Qwen and similar models, ensuring balanced and context-aware outputs.
Event Simulation & Logical Analysis Models random events, probability-driven reasoning, and logical decision-making with strong consistency.
Multilingual Mathematical & General-Purpose Problem Solving Delivers robust performance in math, probability, and structured multilingual tasks, enabling wide applicability in global research and education.
Hybrid Symbolic-Probabilistic Thinking Combines structured logic, probabilistic inference, and reasoning fluency, providing accuracy across uncertainty-driven tasks.
Structured Output Mastery Generates well-structured outputs in LaTeX, Markdown, JSON, CSV, and YAML, supporting technical workflows and data-driven research.
Optimized Lightweight Footprint Large 4B parameter size, deployable on mid-range GPUs, offline clusters, and edge devices, while maintaining reasoning quality.

Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Kepler-Qwen3-4B-Super-Thinking"

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

prompt = "Simulate the probability of rolling two dice and getting a sum greater than 9. Show the reasoning."

messages = [
    {"role": "system", "content": "You are a reasoning tutor skilled in probability, logic, and multilingual problem-solving."},
    {"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)

Intended Use

Balanced multilingual reasoning and probability modeling
Event simulation, uncertainty analysis, and structured problem solving
Educational and research-focused reasoning tasks
Deployment on mid-resource environments with efficient reasoning
Technical content and structured data generation

Limitations

Focused on reasoning and mathematics—less suited for creative writing
Despite 4B size, very complex multi-hop tasks may still challenge the model
Prioritizes structured reasoning and probabilistic accuracy over conversational or emotional tone
May produce inconsistent outputs when handling very long contexts or cross-domain multi-document inputs