license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- reasoning
- math
- coding
- distillation
- small-model
DeepBrainz R1-0.6B
DeepBrainz R1-0.6B is a compact, reasoning-focused language model designed for efficient problem-solving in mathematics, logic, and code-related tasks.
Despite its small size, R1-0.6B emphasizes structured reasoning, stepwise problem decomposition, and stable generation behavior, making it well-suited for research, education, and lightweight deployment scenarios.
Model Highlights
- Compact 0.6B parameter model optimized for efficiency
- Strong focus on reasoning-oriented tasks
- Stable long-form generation for its size class
- Compatible with standard Hugging Face inference tooling
Intended Use
This model is intended for:
- Research and experimentation in reasoning-focused LLMs
- Educational use and demonstrations
- Lightweight inference environments
- Building blocks for agentic or tool-augmented systems
It is not intended as a general-purpose chat replacement for larger frontier models.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "DeepBrainz/deepbrainz-r1-0.6b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
prompt = "Solve step by step: If x + 3 = 7, what is x?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
**inputs,
max_new_tokens=256,
temperature=0.6,
top_p=0.95,
do_sample=True,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Training & Alignment
R1-0.6B was trained using modern post-training techniques emphasizing reasoning quality and generation stability. Specific training details are intentionally abstracted in this public-facing release.
Limitations
Performance is constrained by model size Not optimized for open-ended conversational chat Best for short-to-medium complexity reasoning tasks
License
Apache 2.0
About DeepBrainz
DeepBrainz builds reasoning-first AI systems focused on efficiency, structure, and real-world problem-solving.
More evaluations and updates will follow in future releases.