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WithinUsAI
Available on FriendliAI
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Model Details
Model Provider
WithinUsAI
Model Tree
google/codegemma-1.1-2b
Input Modalities
Output Modalities
Supported Functionality
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 |
vLLM, Ollama, or SGLang.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, AutoModelForCausalLMimport 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 codeprompt = """<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))from transformers import AutoTokenizer, AutoModelForCausalLMimport 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 codeprompt = """<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))