Instructions to use prithivMLmods/Theta-Crucis-0.6B-Turbo1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Theta-Crucis-0.6B-Turbo1 with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/Theta-Crucis-0.6B-Turbo1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Theta-Crucis-0.6B-Turbo1")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Theta-Crucis-0.6B-Turbo1")
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/Theta-Crucis-0.6B-Turbo1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Theta-Crucis-0.6B-Turbo1"
# 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/Theta-Crucis-0.6B-Turbo1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Theta-Crucis-0.6B-Turbo1

SGLang

How to use prithivMLmods/Theta-Crucis-0.6B-Turbo1 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/Theta-Crucis-0.6B-Turbo1" \
    --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/Theta-Crucis-0.6B-Turbo1",
		"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/Theta-Crucis-0.6B-Turbo1" \
        --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/Theta-Crucis-0.6B-Turbo1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Theta-Crucis-0.6B-Turbo1 with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Theta-Crucis-0.6B-Turbo1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Theta-Crucis-0.6B-Turbo1

Theta-Crucis-0.6B-Turbo1 is a compact, high-performance model designed for code generation, technical reasoning, and structured output tasks. Fine-tuned from Qwen3-0.6B using the Mixture of Thoughts (MoT) dataset with an emphasis on code expert clusters, this model delivers agile and accurate coding assistance in low-resource environments. At only 0.6B parameters, it offers strong fluency in programming, structured syntax, and technical language generation.

GGUF: https://huggingface.co/prithivMLmods/Theta-Crucis-0.6B-Turbo1-GGUF

Key Features

MoT Fine-Tuning on Code Expert Clusters Leveraging the Mixture of Thoughts (MoT) dataset, this model is fine-tuned on high-quality programming data across languages, debugging patterns, and code reasoning structures.
Turbo Code Generation & Debugging Excels at generating well-structured, clean code in Python, JavaScript, C++, and more. Capable of explaining logic, identifying bugs, and suggesting improvements.
Structured Output Capabilities Supports outputs in Markdown, JSON, YAML, and LaTeX, making it ideal for auto-documentation, API formatting, and configuration file generation.
Technical Fluency Across Languages Handles code queries and explanations in over 20 languages, enabling global developer support and multilingual documentation.
Lightweight, Inference-Optimized Design Suitable for deployment on edge devices, laptops, or VRAM-limited GPUs, with fast inference and strong accuracy in technical prompts.

Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Theta-Crucis-0.6B-Turbo1"

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

prompt = "Write a Python function that checks if a string is a palindrome. Explain each step."

messages = [
    {"role": "system", "content": "You are an expert code assistant."},
    {"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)