Mesko Legacy V2 access request
This repository is publicly accessible, but you have to accept the conditions to access its files and content.
Mesko Legacy V2 is an untrained closed-architecture research preview. Please share how you plan to use, train, evaluate, or fund this project.
Log in or Sign Up to review the conditions and access this model content.
Mesko Legacy V2 LLM
Mesko Legacy V2 LLM is a closed-architecture assistant-model research preview by MesklinTech. It is published as a gated Hugging Face project to attract training partners, compute sponsors, benchmark contributors, and early reviewers.
Project Snapshot
| Item | Mesko Legacy V2 |
|---|---|
| Release type | Closed-architecture research preview |
| Model state | Untrained V2 concept |
| Access | Manual gated access |
| Goal | Train a proprietary assistant-style LLM from scratch |
| Funding need | GPU compute, dataset preparation, benchmarking, safety testing |
| Architecture detail | Private |
Short Architecture Line
Custom closed-architecture LLM for scalable assistants.
What Makes This Project Strong
- Founder-led and resource-aware: built from the ground up by a small team focused on training under real compute limits.
- Closed-model direction: designed for controlled development similar in spirit to commercial assistant systems.
- Benchmark-first culture: even the earliest release includes a smoke benchmark instead of only a vision statement.
- Gated collaboration: access requests ask for intended use, dataset plan, training stack, compute, benchmark plan, and funding interest.
- Ready for serious partners: the project is structured for compute donors, researchers, labs, and early technical collaborators.
Smoke Benchmark
This is a smoke test of the training path, not a full model-quality benchmark.
| Metric | Result |
|---|---|
| Previous smoke loss | 1.4551 |
| Improved smoke loss | 0.000249 |
| Target loss gate | < 0.3000 |
| Train examples | 4 |
| Validation examples | 2 |
| Epochs | 50 |
| Device | CPU |
| Status | Passed |
The smoke test verifies that the from-scratch training path can optimize, checkpoint, validate, and generate on a tiny controlled dataset.
Comparison With Other LLM Release Styles
| Project style | Example | Public position | How Mesko Legacy V2 differs |
|---|---|---|---|
| Fully open trained LLM | LLM360 K2 / K2-V2 | Trained weights, data, code, and benchmark reports are released for reproducibility. | Mesko Legacy V2 is closed-architecture and currently seeking funding before full training. |
| Preview LLM release | Trillion-style preview projects | Presents a model direction and early release story before broad adoption. | Mesko Legacy V2 adds gated access and asks collaborators to share dataset, training, and benchmark plans. |
| Untrained architecture preview | Small untrained Hugging Face architecture projects | Shares an early architecture/project idea before real training. | Mesko Legacy V2 keeps architecture private and publishes only high-level status plus smoke-test evidence. |
| Commercial closed model | ChatGPT-style closed assistants | Architecture and training stack are not public, but the product is trained and served at scale. | Mesko Legacy V2 follows a closed-model direction but still needs training compute and funding. |
Funding Request
We are seeking donations, sponsorship, compute credits, or research collaboration to train Mesko Legacy V2 properly.
Support will be used for:
- GPU training runs
- legally safe dataset preparation
- tokenizer and data pipeline work
- benchmark reporting
- safety testing
- controlled deployment tooling
If you want to help, request access through this gated repo and mention whether you can contribute compute, funding, benchmark work, or training guidance.
One Clear Limitation
Mesko Legacy V2 is not trained yet, so the current smoke result proves training mechanics only and should not be compared to production benchmark scores.
Responsible Use
Do not market downstream systems as powered by Mesko Legacy V2 until trained weights, evaluation reports, and usage terms are released.