We can talk about the value of LOMA all day.
Or…
You can hear it directly from someone who’s experienced it firsthand.
Here’s what @AbimbolaGaga one of our past speakers, has to say.
If you haven’t registered yet, this is your sign. See registration link in bio.
Today we are Introducing BTL-3.
A 27B open-weight agent model built for agentic coding, structural tool use . The complete thing fits in one 8.39GB file under 2.5 bits per parameter smaller than an 8B model in fp16, and retains 92.2% of the 27B itelligence
BTL-3 is trained for the loop real agents live in: reason, act, inspect the result, recover, continue. It handles single, sequential, and parallel tool calls and knows when the right move is no tool call at all.
HumanEval: 95.12% pass@1
BFCL v4 AST: 88.5% (full 1,240-case set)
Multiple tool calls: 95.5%
Tool-call abstention: 91.2%
262K context architecture
Two editions, both open today.
BTL-3 is the maximum-quality checkpoint, for Transformers and vLLM.
BTL-3 Compact is the entire model in one standalone 8.39GB GGUF. No base download. No reconstruction. One file, one command, a running agent.
Compressing 27B this far normally destroys a model. Standard quantization couldn't do it, so we built the stack ourselves: packed AVQ2 decoder tensors, affine INT4, measured precision islands, packed vocabulary matrices, rank-32 output correction, behavioral repair. 2,416 tensors byte-verified at export.
Then we tested whether the agent survived. On a fresh sealed 100-turn tool-contract gate, Compact retained 92.2% of teacher-correct behavior 100% on single, parallel, sequential, and abstention calls.
43 tok/s generation on an RTX PRO 6000. Fully local. Nothing leaves your machine.
BTL-3: https://t.co/ddZWWr6i3o
Compact: https://t.co/6URHEBGJgG
Runtime + source: https://t.co/MjXQR6koKt
Apache-2.0 model. MIT runtime.
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