Instructions to use SVECTOR-CORPORATION/Theta-35-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SVECTOR-CORPORATION/Theta-35-Preview with Transformers:

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

pipe = pipeline("text-generation", model="SVECTOR-CORPORATION/Theta-35-Preview")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Theta-35-Preview", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SVECTOR-CORPORATION/Theta-35-Preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SVECTOR-CORPORATION/Theta-35-Preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SVECTOR-CORPORATION/Theta-35-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SVECTOR-CORPORATION/Theta-35-Preview

SGLang

How to use SVECTOR-CORPORATION/Theta-35-Preview 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 "SVECTOR-CORPORATION/Theta-35-Preview" \
    --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": "SVECTOR-CORPORATION/Theta-35-Preview",
		"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 "SVECTOR-CORPORATION/Theta-35-Preview" \
        --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": "SVECTOR-CORPORATION/Theta-35-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SVECTOR-CORPORATION/Theta-35-Preview with Docker Model Runner:
```
docker model run hf.co/SVECTOR-CORPORATION/Theta-35-Preview
```

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

You agree to not use the model to conduct experiments that cause harm to human subjects.

Theta-35-Preview: Advanced Logical Reasoning AI Model

Introduction

Theta-35-Preview is an experimental research model developed by SVECTOR, specifically engineered to push the boundaries of logical reasoning and analytical capabilities. This model represents a significant leap in AI technology, designed to tackle complex reasoning tasks with unprecedented precision and depth. As a preview release, it demonstrates promising analytical abilities while having several important limitations:

Language Mixing and Code-Switching: The model may mix languages or switch between them unexpectedly, affecting response clarity. Recursive Reasoning Loops: The model may enter circular reasoning patterns, leading to lengthy responses without a conclusive answer. Safety and Ethical Considerations: The model requires enhanced safety measures to ensure reliable and secure performance, and users should exercise caution when deploying it. Performance and Benchmark Limitations: The model excels in math and coding but has room for improvement in other areas, such as common sense reasoning and nuanced language understanding.

Key Features

Advanced Reasoning Capabilities
- State-of-the-art logical inference
- Deep analytical problem-solving
- Nuanced contextual understanding
Architectural Highlights
- 33 Billion Parameter Model
- Transformer-based architecture
- Advanced attention mechanisms
- Optimized for complex reasoning tasks
Technical Specifications
- Model Type: Causal Language Model
- Parameters: 33 Billion
- Context Length: 32,768 tokens
- Architecture: Advanced Transformer with:
  - RoPE (Rotary Position Embedding)
  - SwiGLU Activation
  - RMSNorm Normalization
  - Enhanced Attention Mechanisms

Performance Capabilities

Exceptional performance in:
- Mathematical reasoning
- Complex problem-solving
- Analytical task decomposition
- Multi-step logical inference

Quickstart Guide

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "SVECTOR-CORPORATION/Theta-35-Preview"

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

# Example reasoning prompt
messages = [
    {"role": "system", "content": "You are an advanced logical reasoning assistant developed by SVector."},
    {"role": "user", "content": "Break down the logical steps to solve a complex problem."}
]

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,
    do_sample=True,
    temperature=0.7
)

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

Ethical AI Commitment

SVECTOR is committed to developing responsible AI that:

Prioritize ethical considerations
Ensure robust safety mechanisms
Promote transparent and accountable AI development

Citation

If you use Theta-35 in your research, please cite:

@misc{theta-35,
    title = {Theta-35: Advanced Logical Reasoning AI Model},
    author = {SVECTOR CORPORATION},
    year = {2025},
    publisher = {SVECTOR}
}

Contact and Support

Website: www.svector.co.in
Email: support@svector.co.in
Research Inquiries: research@svector.co.in

Limitations and Considerations

While Theta-35 represents a significant advancement in AI reasoning, users should be aware of:

Potential context-specific reasoning variations
Need for careful prompt engineering
Ongoing model refinement and updates

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Safetensors

Model size

33B params

Tensor type

BF16

Collection including SVECTOR-CORPORATION/Theta-35-Preview

Theta-35

Collection

Theta-35 is the advanced reasoning model in the Theta series by SVECTOR. • 3 items • Updated Jun 1, 2025 • 7