Instructions to use chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF with jev-style:

pip install jev-style
# GGUF builds score through llama.cpp: build the jev-score binary once
hf download chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF build_jev_score.sh jev_score.cpp --local-dir jev-score
export JEV_SCORE_BIN=$(sh jev-score/build_jev_score.sh /path/to/llama.cpp | tail -n 1)

from jev_style import JevStyle, noul, choice

js = JevStyle.from_pretrained("chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF")
out = js.decide("I was charged twice for one order.", {
    "billing": noul("This message is about billing."),
    "team": choice("Which team should handle it?", ["billing", "shipping", "tech"]),
})
print(out["answers"]["team"]["choice"])

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF:Q4_K_M

Ollama
How to use chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF with Ollama:
```
ollama run hf.co/chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF with Docker Model Runner:
```
docker model run hf.co/chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF:Q4_K_M
```

Lemonade

How to use chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Jev-Style-Qwen3.5-2B-Decision-v2-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Jev-Style-Qwen3.5-2B-Decision-v2-GGUF / Jev-Style-v2-Calibrated-Q8_0.calibration.json

chaoliangUNSW

Rename GGUF files so the quant type ends the filename (Ollama :Q4_K_M tags)

f34035e verified 14 days ago

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	{
	"temperature": 1.0,
	"fitted_temperature_folded": 1.0403540135054734,
	"temperature_folded": true,
	"folded_tensor": "output_norm.weight",
	"source_calibration": {
	"temperature": 1.0403540135054734,
	"calibration_n": 3100,
	"objective": "sample_mean_soft_cross_entropy",
	"bounds": [
	0.05,
	20
	],
	"nll_before": 0.5142612871187658,
	"nll_after": 0.5139652439657095,
	"backend": "gguf",
	"model": "results/Jev-Style-v2-Q8_0.gguf",
	"source_sha256": "2fde7f45dce3440abfde145bb30ad61e2643b1f853866b5760b235685328dc1c"
	},
	"input_sha256": "f659d164d0fed4e645f711cbf56c177ee63c1856e6875f48a27ebac2cfb11175",
	"output_sha256": "5c2aa0d35b24a27f03228b2c62ebaaebd9b5b785844634d4217278d822751494",
	"validation_required": false,
	"validation": {
	"n": 500,
	"argmax_agreement": 0.992,
	"cuda_same_subset": {
	"accuracy": 0.7910177949703642,
	"macro_f1": 0.7760857891780776,
	"nll": 0.6000106706289864,
	"brier": 0.317876961372947,
	"ece": 0.16696329399611096
	},
	"q8_same_subset": {
	"accuracy": 0.7868855635654054,
	"macro_f1": 0.773615445189482,
	"nll": 0.6015417570028033,
	"brier": 0.318930434743987,
	"ece": 0.16630794459650552
	},
	"accuracy_difference": -0.004132231404958775,
	"nll_difference": 0.0015310863738169367,
	"temperature_folded": true,
	"passed": true,
	"practical_deployment_gate_passed": true,
	"initial_strict_accuracy_target_met_on_subset": false,
	"initial_accuracy_loss_target": 0.003,
	"note": "500-example subset check: 99.2% agreement. Observed task-macro loss is 0.413 pp, so the initial 0.3 pp accuracy goal is not met on this subset. Prefer BF16/MLX when accuracy is the priority; this is not a bound on population degradation."
	}
	}