README.md

---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- deepbrainz
- reasoning
- mathematics
- code
- enterprise
- 4b
- long-context
library_name: transformers
---

# DeepBrainz-R1-4B-16K

**DeepBrainz-R1-4B-16K** is a compact, high-performance reasoning model engineered by **DeepBrainz AI & Labs**. Designed for scalability and efficiency, 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:** ~4B
- **Context Window:** 16,384 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-R1-4B-16K"

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))
```

---

## 🏗️ Technical Summary

This model has undergone **post-training** to improve structured reasoning behavior, mathematical problem solving, and robustness in agentic workflows.

*Detailed post-training recipes and dataset compositions are not fully disclosed.*

---

## 🛡️ 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>