File size: 2,671 Bytes
a3afe14 e0814e8 a3afe14 e0814e8 a3afe14 e0814e8 a3afe14 17824a4 a3afe14 e0814e8 a3afe14 e0814e8 a3afe14 e0814e8 a3afe14 e0814e8 a3afe14 e0814e8 a3afe14 e0814e8 a3afe14 3556b6d a3afe14 e0814e8 a3afe14 17824a4 a3afe14 e0814e8 a3afe14 e0814e8 a3afe14 e0814e8 a3afe14 3556b6d e0814e8 a3afe14 e0814e8 a3afe14 e0814e8 a3afe14 e0814e8 a3afe14 e0814e8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | ---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- deepbrainz
- reasoning
- mathematics
- code
- enterprise
- 2b
library_name: transformers
---
# DeepBrainz-R1-2B-16K
**DeepBrainz-R1-2B-16K** 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:** ~2B
- **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-R1-2B-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 enhance reasoning behavior and robustness under agentic workloads.
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>
|