Text Generation
MLX
Safetensors
jev-style
English
qwen3_5
decision-model
classification
calibration
qwen3.5
single-prefill
conversational
Instructions to use chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-MLX-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-MLX-bf16 with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-MLX-bf16") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - jev-style
How to use chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-MLX-bf16 with jev-style:
# Apple silicon pip install "jev-style[mlx]"
from jev_style import JevStyle, noul, choice js = JevStyle.from_pretrained("chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-MLX-bf16") 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
- LM Studio
- MLX LM
How to use chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-MLX-bf16 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-MLX-bf16"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-MLX-bf16" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-MLX-bf16", "messages": [ {"role": "user", "content": "Hello"} ] }' - Atomic Chat
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"protocol": "Post-run MPS sensitivity audit, separate from the original same-CUDA main comparison",
"selection": {
"selected_rendering": "semantic",
"dev_metrics": {
"neutral": {
"accuracy": 0.7714285714285715,
"macro_f1": 0.7459788544523509,
"nll": 0.6996133521392657,
"brier": 0.3261039060421592,
"ece": 0.12216485276160671
},
"semantic": {
"accuracy": 0.7838095238095237,
"macro_f1": 0.7599396892684371,
"nll": 0.718921979422934,
"brier": 0.3180294403591556,
"ece": 0.12064957876186524
}
},
"criterion": "highest real-label dev macro accuracy, then lower dev NLL",
"device": "mps",
"scope": "Post-run sensitivity audit of Laya choice-key formatting, not a new training experiment. No test-based rendering selection."
},
"english_semantic_real_macro": {
"accuracy": 0.7484148342632099,
"macro_f1": 0.7329973270680202,
"nll": 0.6309314045108828,
"brier": 0.3475500737329656,
"ece": 0.12349987305363984
},
"v2_point_wins_vs_english_semantic": 10,
"typed_native_teacher_metrics": {
"accuracy": 0.7475,
"macro_f1": 0.6203131476624142,
"nll": 0.8965023905846662,
"brier": 0.06935947732109757,
"ece": 0.17598656338286872
},
"v2_typed_teacher_metrics": {
"accuracy": 0.7345,
"macro_f1": 0.5843823268841445,
"nll": 0.9071323454613555,
"brier": 0.07585474596137866,
"ece": 0.13429560744677668
},
"native_typed_accuracy_gap_pp": -1.3000000000000012,
"caveats": [
"Development rendering selection used real-label macro accuracy, not test results.",
"This is a device/rendering sensitivity check; do not merge it into the original same-GPU experiment.",
"The typed checkpoint has upstream training exposure to the public train split from which these calibration examples were drawn.",
"Teacher agreement is not real-world correctness; probability matching is described by soft NLL/Brier."
]
}
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