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Model: WithinUsAI/Gemini3.5-Code.Reasoner-2b-Distilled
Source: Original Platform

README.md

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+---
+license: apache-2.0
+tags:
+- text-generation
+- code
+- reasoning
+- codegemma
+- gemma
+- safe-tensors
+- distillation
+- synthetic-dataset
+base_model: google/codegemma-1.1-2b
+datasets:
+- WithinUsAI/GeminiPro3.2_max_distill_god_seed_25k
+- WithinUsAI/gemini_3.5_flash_distilled_25k
+- WithinUsAI/Gemini_3.2_Pro_Distilled
+- WithinUsAI/codegemma_gemini_pro_32_distilled_25k
+- WithinUsAI/DEEPMIND_Alpha_Distilled
+pipeline_tag: text-generation
+library_name: transformers
+language:
+- en
+---
+
+# 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](https://huggingface.co/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`, or `SGLang`.
+
+## 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.
+
+```python
+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))