Instructions to use Luigi/K2-Horizon-0.9B-meeting-agent-zh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Luigi/K2-Horizon-0.9B-meeting-agent-zh with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Luigi/K2-Horizon-0.9B-meeting-agent-zh")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Luigi/K2-Horizon-0.9B-meeting-agent-zh")
model = AutoModelForCausalLM.from_pretrained("Luigi/K2-Horizon-0.9B-meeting-agent-zh", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Luigi/K2-Horizon-0.9B-meeting-agent-zh with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Luigi/K2-Horizon-0.9B-meeting-agent-zh"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Luigi/K2-Horizon-0.9B-meeting-agent-zh",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Luigi/K2-Horizon-0.9B-meeting-agent-zh

SGLang

How to use Luigi/K2-Horizon-0.9B-meeting-agent-zh with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Luigi/K2-Horizon-0.9B-meeting-agent-zh" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Luigi/K2-Horizon-0.9B-meeting-agent-zh",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Luigi/K2-Horizon-0.9B-meeting-agent-zh" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Luigi/K2-Horizon-0.9B-meeting-agent-zh",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Luigi/K2-Horizon-0.9B-meeting-agent-zh with Docker Model Runner:
```
docker model run hf.co/Luigi/K2-Horizon-0.9B-meeting-agent-zh
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

K2-Horizon-0.9B meeting agent (zh-TW), fine-tuned weights (safetensors)

IFM/K2-Horizon-0.9B adapted to zh-TW and fine-tuned as a reading agent: it reads a live zh-TW meeting transcript window by window and writes short cited notes, then turns the notes into a title and a prose summary. This is the best K2 checkpoint of the project (release 2026-10-11, internal tag k2-r16n), full fp32 weights for further fine-tuning or re-export.

GGUF (llama.cpp) build of the same weights: Luigi/K2-Horizon-0.9B-meeting-agent-zh-GGUF
Code, agent protocol and evaluation: https://github.com/vieenrose/meeting-summarizer

file
`model.safetensors`	fp32, 4,375,300,664 bytes, sha256 `e4dd62f51c9d97bf6bcf366432ce4ba532b362348c3fab4bd4d3c9d90be8b9c7`
`config.json`, `generation_config.json`	`LlamaForCausalLM`, `vocab_size` 69312, untied `lm_head`
`tokenizer.json`, `tokenizer_config.json`	extended zh-TW tokenizer (see below)
`chat_template.jinja`	the template used in training and deployment (no BOS, no thinking block)
`system_prompt.txt`	agent system prompt, use verbatim
`title_prompt.txt`, `prose_prompt.txt`	the two end-of-meeting conversion prompts (`{journal}` = the notes, one per line)

How it was built

zh-TW vocabulary + continued pretraining. +5,000 zh-TW BPE merges (about 30 % fewer tokens on zh-TW), continued pretraining on zh-TW web text and meeting transcripts with general replay.
Agent SFT on reading-agent turns distilled from a larger teacher.
On-policy DPO rounds (full fine-tuning, β 0.1, NLL 0.5 on the chosen side, lr ≤ 3e-6), each round on pairs sampled from the current model and ranked by an LLM judge against the transcript: contradicted-note fixes (≈ 2,200 pairs), speaker-binding swaps, prose, titles, and finally missed-key-point "replace" pairs (the nearest wrongly-stated note replaced by the correct one). Conservative DPO (label smoothing 0.1) on the judge-ranked rounds; ~1,000 replay rows of earlier rounds each time.

Results

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.

Use (agent protocol)

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.

from transformers import AutoTokenizer, AutoModelForCausalLM
repo = "Luigi/K2-Horizon-0.9B-meeting-agent-zh"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="bfloat16")

Tokenizer

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).

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Safetensors

Model size

1B params

Tensor type

F32

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