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--- |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: text-generation |
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tags: |
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- deepbrainz |
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- reasoning |
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- mathematics |
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- code |
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- enterprise |
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- 0.6b |
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- long-context |
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library_name: transformers |
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--- |
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# DeepBrainz-R1-0.6B |
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**DeepBrainz-R1-0.6B** 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. |
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This variant features a **32,768 token context window**, optimized for processing medium-to-long documents and codebases. |
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--- |
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## 🚀 Model Highlights |
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- **Parameter Count:** ~0.6B |
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- **Context Window:** 32,768 tokens |
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- **Specialization:** STEM Reasoning, Logic, Code Analysis |
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- **Architecture:** Optimized Dense Transformer |
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- **Deployment:** Ready for vLLM, TGI, and local inference |
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--- |
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## 🎯 Intended Use Cases |
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- **Agentic Workflows:** Reliability in multi-step planning tasks. |
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- **Math & Science:** Solving complex word problems and equations. |
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- **Code Generation:** Writing and debugging algorithms. |
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- **Structured Data Extraction:** Parsing and reasoning over unstructured text. |
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> **Note:** This is a base reasoning model. For conversational chat, we recommend using a specific instruct template or fine-tuning on your domain data. |
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--- |
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## 💻 Usage |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_id = "DeepBrainz/DeepBrainz-R1-0.6B" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_id, |
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torch_dtype="bfloat16", |
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device_map="auto" |
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) |
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prompt = "Analyze the time complexity of the following algorithm:" |
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
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outputs = model.generate(**inputs, max_new_tokens=256) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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--- |
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## 🏗️ Technical Summary |
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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. |
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*Specific training methodologies and dataset compositions are proprietary.* |
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--- |
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## 📜 License |
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This model is released under the **Apache 2.0** license, allowing for academic and commercial use. |
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--- |
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<div align="center"> |
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<b>DeepBrainz AI & Labs</b><br> |
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<i>Advancing General Intelligence through Scalable Reasoning</i> |
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</div> |
