Instructions to use DeepBrainz/DeepBrainz-R1-0.6B-8K-Exp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DeepBrainz/DeepBrainz-R1-0.6B-8K-Exp with Transformers:

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

pipe = pipeline("text-generation", model="DeepBrainz/DeepBrainz-R1-0.6B-8K-Exp")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DeepBrainz/DeepBrainz-R1-0.6B-8K-Exp")
model = AutoModelForCausalLM.from_pretrained("DeepBrainz/DeepBrainz-R1-0.6B-8K-Exp")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DeepBrainz/DeepBrainz-R1-0.6B-8K-Exp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DeepBrainz/DeepBrainz-R1-0.6B-8K-Exp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DeepBrainz/DeepBrainz-R1-0.6B-8K-Exp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DeepBrainz/DeepBrainz-R1-0.6B-8K-Exp

SGLang

How to use DeepBrainz/DeepBrainz-R1-0.6B-8K-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 "DeepBrainz/DeepBrainz-R1-0.6B-8K-Exp" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DeepBrainz/DeepBrainz-R1-0.6B-8K-Exp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "DeepBrainz/DeepBrainz-R1-0.6B-8K-Exp" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DeepBrainz/DeepBrainz-R1-0.6B-8K-Exp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DeepBrainz/DeepBrainz-R1-0.6B-8K-Exp with Docker Model Runner:
```
docker model run hf.co/DeepBrainz/DeepBrainz-R1-0.6B-8K-Exp
```

DeepBrainz-R1-0.6B-8K-Exp

File size: 2,465 Bytes

---
license: apache-2.0
language:
  - en
pipeline_tag: text-generation
tags:
  - deepbrainz
  - reasoning
  - mathematics
  - code
  - enterprise
  - 0.6b
library_name: transformers
---

# DeepBrainz-R1-0.6B-8K-Exp

**DeepBrainz-R1-0.6B-8K-Exp** is a compact, high-performance reasoning model engineered by **DeepBrainz AI & Labs**. Designed for efficiency and scalability, it specializes in structured chain-of-thought reasoning, mathematical problem solving, and logical analysis.

This model is part of the **DeepBrainz-R1 Series**, built to deliver frontier-class reasoning capabilities in cost-effective parameter sizes.

---

## 🚀 Model Highlights

- **Parameter Count:** ~0.6B
- **Context Window:** 8,192 tokens
- **Specialization:** STEM Reasoning, Logic, Code Analysis
- **Architecture:** Optimized Dense Transformer (Qwen2.5/3 Compatible)
- **Deployment:** Ready for vLLM, TGI, and local inference

---

## 🎯 Intended Use Cases

- **Agentic Workflows:** Reliability in multi-step planning tasks.
- **Math & Science:** Solving complex word problems and equations.
- **Code Generation:** Writing and debugging algorithms.
- **Structured Data Extraction:** Parsing and reasoning over unstructured text.

> **Note:** This is a post-trained reasoning variant intended for evaluation and experimentation.  
> It is not production-validated and is not optimized for open-ended conversational chat.

---

## 💻 Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "DeepBrainz/DeepBrainz-R1-0.6B-8K-Exp"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="bfloat16",
    device_map="auto"
)

prompt = "Analyze the time complexity of the following algorithm:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

---

## 🛡️ Limitations & Safety

While this model demonstrates strong reasoning capabilities, it may still produce inaccurate information ("hallucinations"). Users should implement appropriate guardrails for production deployments.

---

## 📜 License

This model is released under the **Apache 2.0** license, allowing for academic and commercial use.

---

<div align="center">
  <b>DeepBrainz AI & Labs</b><br>
  <i>Advancing General Intelligence through Scalable Reasoning</i>
</div>