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

chaoliangUNSW commited on 13 days ago

Commit

096600f

verified ·

1 Parent(s): 63f2f24

README: add Ollama usage; SHA256SUMS: update README, add template/params

Browse files

Files changed (2) hide show

README.md +8 -0
SHA256SUMS.json +10 -2

README.md CHANGED Viewed

@@ -154,6 +154,14 @@ Use a llama.cpp build with Qwen3.5 support. Conversion and native evaluation use
 **Runtime calibration temperature is 1.0** for this file: its fitted temperature has already been incorporated. The accompanying calibration JSON records the exact settings and checksum. Serve the raw decision prompt shown below, with the full declared option list.
 ## Decision interface

 **Runtime calibration temperature is 1.0** for this file: its fitted temperature has already been incorporated. The accompanying calibration JSON records the exact settings and checksum. Serve the raw decision prompt shown below, with the full declared option list.
+### Ollama
+```bash
+ollama run hf.co/chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF:Q4_K_M
+```
+Send the raw decision prompt shown below as the message; the model replies with the letter of the selected option (` B` for the example). Replace `Q4_K_M` with `Q8_0` or `BF16` for another precision. This repository supplies its own Ollama template, so run `ollama pull` again if you pulled the model before 2026-09-25.
 ## Decision interface

SHA256SUMS.json CHANGED Viewed

@@ -12,8 +12,8 @@
     "sha256": "50cbab8a892c5f2993b8c7351a99182507472def3b1374558308605d99b86b32"
   },
   "README.md": {
-    "bytes": 12086,
-    "sha256": "5d8614413dc1583b450b81ebeee569907a023c4c65d5752dd318f542b9071577"
   },
   "evaluation/baseline_sensitivity.json": {
     "bytes": 2015,
@@ -122,5 +122,13 @@
   "evaluation/bf16_http_smoke.json": {
     "bytes": 690,
     "sha256": "d729e4dd7ab5d425e5eb031ca1dc92d74302a7971d9aeeba782c15e4904a5f59"
   }
 }

     "sha256": "50cbab8a892c5f2993b8c7351a99182507472def3b1374558308605d99b86b32"
   },
   "README.md": {
+    "bytes": 12508,
+    "sha256": "406e98c0183ed8033b9c5db744454f06c5a45321b82aa024f3051949f65be2ae"
   },
   "evaluation/baseline_sensitivity.json": {
     "bytes": 2015,
   "evaluation/bf16_http_smoke.json": {
     "bytes": 690,
     "sha256": "d729e4dd7ab5d425e5eb031ca1dc92d74302a7971d9aeeba782c15e4904a5f59"
+  },
+  "template": {
+    "bytes": 91,
+    "sha256": "244d5cfdeb97383239af17d352fc32c2123257b2eecda8fd8285a7cd29770648"
+  },
+  "params": {
+    "bytes": 31,
+    "sha256": "208f455e3685ae841dea00e45a06d3b7fd226a0fbf88e6f2cd1077ff8e6b36af"
   }
 }