---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- deepbrainz
- reasoning
- mathematics
- code
- enterprise
- 4b
- long-context
- 40k
library_name: transformers
---

# DeepBrainz-R1-4B-40K

**DeepBrainz-R1-4B-40K** is a compact, high-performance reasoning model engineered by **DeepBrainz AI & Labs**. It is part of the **DeepBrainz-R1 Series**, designed to deliver frontier-class reasoning capabilities in cost-effective parameter sizes.

This specific variant offers a **40,960 token context window**, making it suitable for `maximum context version designed for repository-level code reasoning.`.

---

## 🚀 Model Highlights

- **Parameter Count:** ~4B
- **Context Window:** 40,960 tokens
- **Context Type:** Extended (RoPE)
- **Specialization:** STEM Reasoning, Logic, Code Analysis
- **Architecture:** Optimized Dense Transformer
- **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 base reasoning model. For conversational chat, we recommend using a specific instruct template or fine-tuning on your domain data.

---

## 💻 Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "DeepBrainz/DeepBrainz-R1-4B-40K"

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

The model was produced using a **multi-stage optimization process** involving large-scale supervision and iterative refinement. It is designed to maximize reasoning quality while maintaining instruction robustness.

*Specific training methodologies and dataset compositions are proprietary.*

---

## 🛡️ 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>