i need your help.
Technical Optimization Request: Gemma 4 E4B IT (LiteRT / 1M Context)
Hello mradermacher,
I am writing to submit a technical request regarding the optimization of the Gemma 4 E4B IT model. I have been following your contributions to the open-weights ecosystem and value the precision you bring to model quantization and format conversion.
Context and Objective:
As an individual with autism, my professional focus lies in conceptual thinking and high-level logic rather than manual syntax execution. To bridge this gap, I am developing an "AI Agent" based workflow. To make this sustainable, I require a local model that maximizes hardware efficiency while maintaining frontier-level reasoning capabilities.
Specifications:
I am requesting an optimization of Gemma 4 E4B IT specifically for the LiteRT (.litertlm) format. While GGUF is common, LiteRT provides the necessary orchestration for my specific edge-deployment environment.
Key Technical Requirements:
- 1M Context Extension (Reach Goal): I am highly interested in seeing if context-extension techniques (such as RoPE scaling) can be applied to reach a 1M token window, enabling repository-wide reasoning within agentic loops.
- Architectural Preservation: The optimization must ensure no degradation of the model’s core features, including the 128K base context, 262K vocabulary efficiency, and native multimodal (image/audio) processing.
- Agentic Precision: High fidelity in tool-calling and instruction following is essential for autonomous workflows.
- Unfiltered Reasoning: A version that minimizes moralizing constraints is requested to ensure the model functions as a neutral, high-utility reasoning engine.
- Bilingual Fidelity: Performance in both English and Turkish must remain at frontier levels.
Final Note on Feasibility:
I am fully aware of the technical complexity and the resource allocation required for such an optimization, especially concerning the 1M context extension in LiteRT. If you find this request outside your current scope or technically unfeasible at this time, I completely understand and respect that decision.
I would like to extend my sincere thanks to you for your individual efforts in the field, and to the broader open-source community for making these advanced tools accessible to everyone.
Regards.
Hey, I appreciate your interest. There is a slight issue: we only do gguf models as you can notice and we are not producing other quantizations or any forks of llamacpp. Only main llamacpp version, Im sorry to disappoint =(
For your help I could recommend you a few services: google colab and kaggle. There you can get some of free resources which you can use to quantize the models yourself. Good luck in your research, let me know if you have any requests =)
Hey, I appreciate your interest. There is a slight issue: we only do gguf models as you can notice and we are not producing other quantizations or any forks of llamacpp. Only main llamacpp version, Im sorry to disappoint =(
For your help I could recommend you a few services: google colab and kaggle. There you can get some of free resources which you can use to quantize the models yourself. Good luck in your research, let me know if you have any requests =)
i wrote my request with ai, i mean something is not important i mean i want use this model, i am use the lms alreadey with gguf, thanks for awnser.