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---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- qwen3
- reasoning
- long-context
- enterprise
- research
- conversational
---
# DeepBrainz-R1-4B-16K
DeepBrainz-R1-4B-16K is a compact, long-context reasoning model in the **DeepBrainz-R series**, designed for structured problem-solving, analysis, and enterprise research workflows.
The model emphasizes **reasoning quality, instruction robustness, and stability over long contexts**, while remaining efficient to deploy on modern GPU inference runtimes.
---
## Model Highlights
- ~4B parameters
- 16K context length
- Optimized for reasoning-centric math and coding tasks
- Designed for modern GPU inference runtimes
- **Architecture:** Qwen3-compatible (DeepBrainz-R series, post-trained, and optimized for reasoning-centric workloads)
---
## Intended Use
- Advanced reasoning systems
- Math and Coding
- Research and evaluation
- Agentic workflows
- Inference-time scaling and test-time compute experiments
**Not intended** as a general-purpose chat replacement for large frontier models.
---
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "DeepBrainz/DeepBrainz-R1-4B-16K"
tok = AutoTokenizer.from_pretrained(model_id)
mdl = AutoModelForCausalLM.from_pretrained(model_id)
prompt = "Solve step by step: If x + 5 = 12, what is x?"
inputs = tok(prompt, return_tensors="pt")
out = mdl.generate(
**inputs,
max_new_tokens=256,
do_sample=True,
temperature=0.6,
top_p=0.95,
)
print(tok.decode(out[0], skip_special_tokens=True))
```
---
## Training Summary
The model was produced using a **multi-stage optimization process** involving large-scale on-policy optimization and **iterative refinement** to improve reasoning quality and robustness.
Specific training details are intentionally abstracted in this public release.
---
## Limitations
Performance depends on task complexity and inference configuration.
Larger models may outperform R1-4B-16K on extremely complex tasks.
---
## License
Apache 2.0
---
## About DeepBrainz
DeepBrainz builds reasoning-first AI systems focused on efficiency, structure, and real-world problem-solving. |