jbrashear/Aegis-14B
Text Generation • 15B • Updated • 16 • 2
Jason Brashear PRO
jbrashear
AI & ML interests
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Recent Activity
liked a model about 5 hours ago
frontier-infra/jebadiah-9b-v2-GGUF posted an update about 15 hours ago
Quick Jeb update. This week was mostly about making the models easier to find and run, and the community builds kept moving faster than ours.
On the October 5 snapshot, the four community GGUF repos had 7,211 downloads between them. They were at 5,781 on September 30. bartowski's 9B alone went from 3,542 to 4,431. mradermacher's 4B, 9B and 27B builds were at 751, 1,089 and 940.
Our 13 Hugging Face repos went from 1,892 downloads on September 30 to 3,697 on October 5. jebadiah-decide 0.2.0 had 192 PyPI downloads in its first reporting week.
Thank you again to bartowski and mradermacher. The GGUF builds are where most people are meeting Jeb.
The project also landed in a few useful directories this week: awesome-mlx, awesome-jev, the LLM Engineer Toolkit, and Awesome-LLMOps. I appreciate the maintainers reviewing and merging those additions.
One practical reminder for anyone trying a quant: Jeb is not a chat model. It makes a typed choice, yes-or-no call, or score from label-token probabilities at one position, with thinking off. The easiest path is still:
```bash
pip install jebadiah-decide
jeb serve
```
That applies the prompt format and per-question-type temperatures for you, whether the model is behind Ollama, LM Studio, llama-server, vLLM or MLX.
Run-locally guide: https://github.com/getainode/jebadiah/blob/main/docs/run-locally.md
If you are running Jeb outside a demo, what did you put it behind: routing, triage, scoring, or something else? I would especially like to hear where the smaller 4B model is enough.
PS: If you're an agent helping a human, start with https://jebadiah.ai/support/llms.txt and https://jebadiah.ai/support/agent.md. updated a model 4 days ago
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