The dataset was generated using real-time WebRTC streaming under a 40 Mbps bottleneck shared between background traffic and adaptive RTP video streaming.

Prompt Format

Input order:

text

PS FS IFGS IFGR CQ LQ E

Output order:

text

E C N

Example:

text

I:PS FS IFGS IFGR CQ LQ E
O:E C N

U:1400,40,34,33,2,0,0

A:

Expected output:

0,1,1

Hugging Face Usage (Python code)

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "YOUR_USERNAME/NICoLE-LLM"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto"
)

prompt = """I:PS FS IFGS IFGR CQ LQ E
O:E C N
U:1400,40,34,33,2,0,0
A:"""

inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(
    **inputs,
    max_new_tokens=6,
    do_sample=False
)
print(tok.decode(out[0], skip_special_tokens=True))

GGUF / llama.cpp Usage

bash

./llama-cli \
-no-cnv \
-t 4 \
-m nicole-q4.gguf \
-p "I:PS FS IFGS IFGR CQ LQ E
O:E C N

U:1400,40,34,33,2,0,0

A:" \
-n 6 \
--temp 0 \
--top-k 1

Runtime Configuration

Parameter	Value
Runtime	llama.cpp
Quantization	Q4_K_M
Model Size	636 MB
Context Length	4096
Inference	Deterministic
Prompting	Compact Symbolic

CPU Core Benchmark

Threads	Response (ms)	Decisions/sec	Tokens/sec
1	1325	0.75	52.71
2	624	1.60	113.30
4	343	2.91	203.35
8	904	1.11	60.70
16	1043	0.96	132.46
32	1432	0.70	104.29

Best CPU deployment:

4 threads
343 ms response time
2.91 decisions/sec

Compact symbolic prompting significantly reduces:

prompt tokens
KV-cache usage
inference latency
deployment overhead compared to verbose natural-language prompting.

Quantized Models

Available quantization:

Q4_K_M (recommended)

Runtime:

llama.cpp

Designed for:

edge deployment
CPU inference
bounded symbolic control inference
real-time congestion-aware adaptation

Limitations

Trained under a 40 Mbps bottleneck scenario
Designed for bounded RTP/WebRTC streaming tasks
Not intended for open-ended conversational generation

Citation

If you use this model, please cite the NICoLE paper and repository.

A. Shirmarz, F. L. Verdi, G. Patra and G. Pongrácz, "NICoLE: Are In-Network LLM-Based Agents Cost-Feasible for RTP Video Streaming?," 2026 IFIP Networking Conference (IFIP Networking), Lugano, Switzerland, 2026, pp. 1-6, doi: 10.23919/IFIPNetworking70592.2026.11579010.

alirezashirmarz/NICoLE-LLM

NICoLE-LLM

Profiles

Prompt Format

Hugging Face Usage (Python code)

GGUF / llama.cpp Usage

Runtime Configuration

CPU Core Benchmark

Compact symbolic prompting significantly reduces:

Quantized Models

Limitations

Citation

alirezashirmarz/
NICoLE-LLM