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 tasks
- Designed for modern GPU inference runtimes
- Architecture: Qwen3-compatible (DeepBrainz-R series, optimized via OPD)
Intended Use
- Advanced reasoning systems
- 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
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 supervision 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.