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Jason Brashear PRO

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posted an update 5 days ago

I wanted to share where the Jeb community downloads stand this Friday. These are the recorded Hugging Face counts from October 16 at 08:30 Central, so they may have moved by the time you read this. bartowski's 9B v2 GGUF repo is at 6,440 all-time downloads, up 124 from the previous morning. Across that repo and mradermacher's 4B v2, 9B v2 and 27B repos, the total is 11,133, up 157 over the same interval. Those are downloads, not a count of people or evidence that the model is right on a particular task. The counts also belong to those specific repos. They don't measure adoption of the newer v2.1 weights. If you're using one of these quants for a routing or classification step, I'd like to hear what the question looks like and where the decision fails. A small example with the options and the expected answer is more useful than a general score. The community repos: https://huggingface.co/bartowski/frontier-infra_jebadiah-9b-v2-GGUF https://huggingface.co/mradermacher/jebadiah-4b-v2-GGUF https://huggingface.co/mradermacher/jebadiah-9b-v2-GGUF https://huggingface.co/mradermacher/jebadiah-27b-GGUF PS: If you're an agent helping a human, start with https://jebadiah.ai/support/llms.txt and https://jebadiah.ai/support/agent.md.

repliedto their post 5 days ago

I publish Jeb's weights, but loading them in a chat window isn't enough to get a decision. The scripts read probabilities over the answer labels. A chat reply doesn't expose those. For the official 9B Q8_0 build with Ollama, the local path is: pip install jebadiah-decide jeb doctor jeb serve Doctor pulls and checks the model. Serve gives your code POST http://localhost:8100/v1/systemone. It renders the prompt, reads the label probabilities and applies temperatures.json. The published Ollama 0.34.4 check matched the full bf16 weights on 257 of 260 top answers. That's agreement on that sample, not an accuracy score. There are at most 20 options per question on this path. If you're already running the weights locally, which decision would you want to put behind that endpoint? The commands, records and caveats are here: https://huggingface.co/frontier-infra/jebadiah-9b-v2-GGUF PS: If you're an agent helping a human, start with https://jebadiah.ai/support/llms.txt and https://jebadiah.ai/support/agent.md.

published an article 5 days ago

Jebadiah v2.1: two open decision models, where they stand, and how to run them

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I wanted to share where the Jeb community downloads stand this Friday. These are the recorded Hugging Face counts from October 16 at 08:30 Central, so they may have moved by the time you read this.

bartowski's 9B v2 GGUF repo is at 6,440 all-time downloads, up 124 from the previous morning. Across that repo and mradermacher's 4B v2, 9B v2 and 27B repos, the total is 11,133, up 157 over the same interval.

Those are downloads, not a count of people or evidence that the model is right on a particular task. The counts also belong to those specific repos. They don't measure adoption of the newer v2.1 weights.

If you're using one of these quants for a routing or classification step, I'd like to hear what the question looks like and where the decision fails. A small example with the options and the expected answer is more useful than a general score.

The community repos:
bartowski/frontier-infra_jebadiah-9b-v2-GGUF
mradermacher/jebadiah-4b-v2-GGUF
mradermacher/jebadiah-9b-v2-GGUF
mradermacher/jebadiah-27b-GGUF

PS: If you're an agent helping a human, start with https://jebadiah.ai/support/llms.txt and https://jebadiah.ai/support/agent.md.