Instructions to use prithivMLmods/Capella-Qwen3-DS-V3.1-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Capella-Qwen3-DS-V3.1-4B with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/Capella-Qwen3-DS-V3.1-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Capella-Qwen3-DS-V3.1-4B")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Capella-Qwen3-DS-V3.1-4B", 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 prithivMLmods/Capella-Qwen3-DS-V3.1-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Capella-Qwen3-DS-V3.1-4B"
# 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/Capella-Qwen3-DS-V3.1-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Capella-Qwen3-DS-V3.1-4B

SGLang

How to use prithivMLmods/Capella-Qwen3-DS-V3.1-4B 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/Capella-Qwen3-DS-V3.1-4B" \
    --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/Capella-Qwen3-DS-V3.1-4B",
		"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/Capella-Qwen3-DS-V3.1-4B" \
        --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/Capella-Qwen3-DS-V3.1-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Capella-Qwen3-DS-V3.1-4B with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Capella-Qwen3-DS-V3.1-4B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Capella-Qwen3-DS-V3.1-4B

Capella-Qwen3-DS-V3.1-4B is a reasoning-focused model fine-tuned on Qwen-4B using DeepSeek v3.1 synthetic traces (10K). It specializes in random event simulations, logical problem analysis, and structured reasoning tasks. The model blends symbolic precision, probabilistic logic, and structured output fluency—making it an ideal tool for researchers, educators, and developers working with uncertainty modeling and event-driven analysis.

GGUF: https://huggingface.co/prithivMLmods/Capella-Qwen3-DS-V3.1-4B-GGUF

Key Features

Event Simulation & Logical Analysis Fine-tuned on 10,000 synthetic traces from DeepSeek v3.1 to model random events, probability-driven reasoning, and logical decision-making.
Advanced Code Reasoning & Generation Supports multi-language coding with explanations, optimization hints, and error detection—ideal for algorithm synthesis, stochastic simulations, and debugging.
Mathematical & Probabilistic Problem Solving Performs analytical reasoning across probability, statistics, and mathematics—explaining concepts, solving equations, and simulating uncertain outcomes.
Hybrid Symbolic-Probabilistic Thinking Combines structured logic, probabilistic inference, and chain-of-thought reasoning, delivering robust performance on uncertainty-driven tasks.
Structured Output Mastery Seamlessly generates output in LaTeX, Markdown, JSON, CSV, and YAML, suited for technical documentation, simulations, and structured analysis.
Optimized Lightweight Footprint for Versatile Deployment Balances performance and efficiency, making it deployable on mid-range GPUs, offline clusters, and edge AI systems.

Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Capella-Qwen3-DS-V3.1-4B"

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 coding."},
    {"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)