85 lines
3.7 KiB
Markdown
85 lines
3.7 KiB
Markdown
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---
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license: apache-2.0
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tags:
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- text-generation
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- code
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- reasoning
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- codegemma
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- gemma
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- safe-tensors
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- distillation
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- synthetic-dataset
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base_model: google/codegemma-1.1-2b
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datasets:
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- WithinUsAI/GeminiPro3.2_max_distill_god_seed_25k
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- WithinUsAI/gemini_3.5_flash_distilled_25k
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- WithinUsAI/Gemini_3.2_Pro_Distilled
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- WithinUsAI/codegemma_gemini_pro_32_distilled_25k
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- WithinUsAI/DEEPMIND_Alpha_Distilled
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pipeline_tag: text-generation
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library_name: transformers
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language:
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- en
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---
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# Gemini3.5-Code.Reasoner-2b-Distilled
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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.
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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.
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## Model Details
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- **Developed by:** WithinUsAI
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- **Model Type:** Causal Language Model (Fine-tuned / Knowledge Distilled)
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- **Base Model:** [google/codegemma-1.1-2b](https://huggingface.co/google/codegemma-1.1-2b)
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- **Architecture:** GemmaForCausalLM (CodeGemma variant) + LoRA Adapters
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- **License:** Apache 2.0
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## Training & Dataset Recipe
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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:
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| Dataset Name | Source / Focus | Approx. Size |
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| :--- | :--- | :--- |
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| `WithinUsAI/GeminiPro3.2_max_distill_god_seed_25k` | High-quality frontier seed prompts for code generation. | ~25k samples |
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| `WithinUsAI/gemini_3.5_flash_distilled_25k` | Fast, iterative logical steps and multi-turn debugging data. | ~25k samples |
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| `WithinUsAI/Gemini_3.2_Pro_Distilled` | Heavy math logic, structural coding, and system design patterns. | Premium corpus |
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| `WithinUsAI/codegemma_gemini_pro_32_distilled_25k` | Target-aligned distillation data optimized for the CodeGemma vocabulary. | ~25k samples |
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| `WithinUsAI/DEEPMIND_Alpha_Distilled` | Deep algorithmic competitive programming and math reasoning. | Premium corpus |
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## Intended Use
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- **Local Code Assistants:** Ideal for IDE plugins requiring fast, low-latency code completion and instruction following.
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- **Logical Chain-of-Thought:** Designed to output its reasoning process before writing the final code block, minimizing syntax and logical errors.
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- **Resource-Constrained Environments:** Can easily be deployed on mobile devices, single-GPU setups, or local laptops using frameworks like `vLLM`, `Ollama`, or `SGLang`.
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## Quickstart Guide
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### Inference with Hugging Face Transformers
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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.
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "WithinUsAI/Gemini3.5-Code.Reasoner-2b-Distilled"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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# Prompt the model to think step-by-step before delivering code
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prompt = """<bos>Analyze the problem and think step-by-step before writing any code.
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Problem: Write a Python generator function that yields the Fibonacci sequence up to n elements.
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Answer:"""
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.2)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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