OpenEuroLLM / OpenEuroLLM/Taskboard
Post-flagship: explore the architecture that is optimal for deployment
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Description
Goal
Raised in a model exploration meeting: what can we try to attempt to come up with the most optimal architecture. The issue is considered to be exploratory task for post-flagship.
Description
This includes
- efficient long context handling
- sliding window attention
- hybrid (eg Qwen 3.5 design, gated delta net layers)
- aim for 1M context length in final version?
- Quantization aware (easy to quantize to lower precision, eg NVFP4); easy GGUF conversion; llama.cpp compat, etc
TODO: add possible modifications list to the task - re-do weak scaling now
Deliverable scope
(What is the expected outcome for this issue to be complete)
Dependencies
(Blocking issues, PRs, or external factors)
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
The issue names no files, tests, or entry points. Start by defining the missing deliverable and narrowing the listed options: long-context handling, sliding-window or hybrid attention, quantization, conversion compatibility, and weak scaling. Done should include a documented architecture choice, its evaluation criteria, and the requested weak-scaling results.
Written by the indexing model from the issue text.
Assessment
- Domain
- machine-learning, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100