Gemini3.5-Code.Reasoner-2b-Distilled
Gemini3.5-Code.Reasoner-2b-Distilled is a highly efficient, reasoning-dense model tailored for advanced coding tasks, algorithmic problem-solving, and logical chain-of-thought workflows.
By applying a specialized Low-Rank Adaptation (LoRA) layer over CodeGemma 1.1 2B, this model infuses frontier-level reasoning mechanics into a compact, 2-billion parameter architecture. It bridges the gap between massive cloud-hosted models and local, edge-compute hardware.
Model Details
- Developed by: WithinUsAI
- Model Type: Causal Language Model (Fine-tuned / Knowledge Distilled)
- Base Model: google/codegemma-1.1-2b
- Architecture: GemmaForCausalLM (CodeGemma variant) + LoRA Adapters
- License: Apache 2.0
Training & Dataset Recipe
The "Reasoner" capabilities of this model are distilled from a multi-source synthetic pipeline focusing on complex coding logic, algorithmic optimization, and step-by-step thinking patterns. The training mixture leverages approximately 100K+ high-quality reasoning examples across five core datasets:
| Dataset Name | Source / Focus | Approx. Size |
|---|---|---|
WithinUsAI/GeminiPro3.2_max_distill_god_seed_25k | High-quality frontier seed prompts for code generation. | ~25k samples |
WithinUsAI/gemini_3.5_flash_distilled_25k | Fast, iterative logical steps and multi-turn debugging data. | ~25k samples |
WithinUsAI/Gemini_3.2_Pro_Distilled | Heavy math logic, structural coding, and system design patterns. | Premium corpus |
WithinUsAI/codegemma_gemini_pro_32_distilled_25k | Target-aligned distillation data optimized for the CodeGemma vocabulary. | ~25k samples |
WithinUsAI/DEEPMIND_Alpha_Distilled | Deep algorithmic competitive programming and math reasoning. | Premium corpus |
Intended Use
- Local Code Assistants: Ideal for IDE plugins requiring fast, low-latency code completion and instruction following.
- Logical Chain-of-Thought: Designed to output its reasoning process before writing the final code block, minimizing syntax and logical errors.
- Resource-Constrained Environments: Can easily be deployed on mobile devices, single-GPU setups, or local laptops using frameworks like
vLLM,Ollama, orSGLang.
Quickstart Guide
Inference with Hugging Face Transformers
Because CodeGemma utilizes specialized tokens for coding workflows, it's recommended to structure your prompts cleanly to prompt the model's inner chain-of-thought.
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "WithinUsAI/Gemini3.5-Code.Reasoner-2b-Distilled"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto"
)
# Prompt the model to think step-by-step before delivering code
prompt = """<bos>Analyze the problem and think step-by-step before writing any code.
Problem: Write a Python generator function that yields the Fibonacci sequence up to n elements.
Answer:"""
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.2)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))