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Luigi
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Model Details
Model Provider
Luigi
Model Tree
IFM/K2-Horizon-0.9B
Input Modalities
Output Modalities
Supported Functionality
38 held-out zh-TW meetings transcribed by ASR, 4k context, window 1,500 tokens, GGUF Q8_0 on llama.cpp, student decoding T 0.2; judge Gemma-4-31B checks each cited statement against the transcript. Two full runs (run-to-run noise ≈ ±2 points / ±0.015 coverage).
| run 1 | run 2 | target (fine-tuned Gemma-4-E2B) | |
|---|---|---|---|
| notes contradicted by the transcript | 13 % | 10–12 % | 17 % |
| notes unsupported | 1–2 % | 3 % | 5 % |
| coverage of the reference key points | 0.92 | 0.89 | 0.92 |
| title score (1–5, mean of 4 runs) | 4.15 | 4.05 | |
| prose sentences supported by the notes | 87.7 % | 88 % | 86.6 % |
The Gemma-4-E2B reference was measured with its mobile runtime, this model with llama.cpp Q8_0. Prefer 8-bit: int4 cost about 7 points of contradiction in our tests.
One fresh conversation per transcript window (about 1,500 tokens) with the compacted journal of earlier notes
at its head; the model answers with at most 5 lines NOTE [time] (TYPE) text (or REVISE #id [time] text), then NEXT.
Stop at NEXT and cap replies at about 400 tokens. Types: DECISION, ACTION, PROPOSAL, OPEN-ISSUE, NUMBER, -.
Rendered note line, as fed back in the journal and to the conversion prompts: #1 [1:23] (DECISION) 預算照案通過.
The title and prose prompts are single user turns with no system turn.
python
from transformers import AutoTokenizer, AutoModelForCausalLMrepo = "Luigi/K2-Horizon-0.9B-meeting-agent-zh"tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="bfloat16")Extended vocabulary, not the base IFM tokenizer: 5,000 zh-TW merges at ids 64256–69255; 69,256 tokens,
embedding matrix padded to 69,312 rows. bos <|begin_of_text|> 0 (the model was trained without BOS; the chat template adds none),
<|ifm|im_start|> 64018, <|ifm|im_end|> (eos) 64019, pad 64255. Never pair these weights with the base
tokenizer: ids above 64255 differ.
No training data is included. License: Apache-2.0 (base model).
from transformers import AutoTokenizer, AutoModelForCausalLMrepo = "Luigi/K2-Horizon-0.9B-meeting-agent-zh"tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="bfloat16")