Text Classification
Transformers
Safetensors
English
qwen3_5_text
text-generation
system-one
system-two
blocks-of-experts
typed-decisions
decision-model
calibrated-probabilities
knowledge-distillation
jev
noul
choice
score
lora
qwen3_5
dual-head
vllm
Eval Results (legacy)
Instructions to use autotrust/JEV-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autotrust/JEV-27B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autotrust/JEV-27B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("autotrust/JEV-27B") model = AutoModelForCausalLM.from_pretrained("autotrust/JEV-27B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
serve_decide.py: System 1 only by default for every model (thinking is opt-in)
Browse files- serve_decide.py +3 -3
serve_decide.py
CHANGED
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@@ -12,8 +12,8 @@ POST /v1/decide
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options list of strings (choice only)
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strategy choices with more than 16 options: "single" (one pass, labels A-P then Q-Z, AA, ...), "tournament"
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(groups of <=16 + a final of 16), "permute" (single pass over 4 option orders, averaged); default per model
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thinking "off", "auto" (think only when the leading option is below `threshold`),
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threshold System 1 confidence below which "auto" switches thinking on (default per model)
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reasoning controls, as for the base model's own chat API:
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chat_template_kwargs passed to the base model's chat template (e.g. Qwen3.8: {"reasoning_effort": "low"})
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@@ -65,7 +65,7 @@ PROFILES = {
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"qwen": {"prefix": "", "image": "<|vision_start|><|image_pad|><|vision_end|>", "end_think": "</think>",
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"after_think": "\n\n", "strategy": "single", "threshold": 0.8, "mix": 0.5, "thinking": "off"},
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"gemma": {"prefix": "<bos>", "image": "<|image|>", "end_think": "<channel|>", "after_think": "",
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"strategy": "tournament", "threshold": 0.8, "mix": 0.5, "thinking": "
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}
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# Read the full distribution: override generation_config defaults (top_k/top_p) that would truncate processed logprobs.
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READ = dict(max_tokens=1, temperature=1.0, top_p=1.0, top_k=0, min_p=0.0, repetition_penalty=1.0,
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options list of strings (choice only)
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| 13 |
strategy choices with more than 16 options: "single" (one pass, labels A-P then Q-Z, AA, ...), "tournament"
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| 14 |
(groups of <=16 + a final of 16), "permute" (single pass over 4 option orders, averaged); default per model
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| 15 |
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thinking "off" (default: System 1 only), "auto" (think only when the leading option is below `threshold`),
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+
"on" (always think)
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| 17 |
threshold System 1 confidence below which "auto" switches thinking on (default per model)
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| 18 |
reasoning controls, as for the base model's own chat API:
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| 19 |
chat_template_kwargs passed to the base model's chat template (e.g. Qwen3.8: {"reasoning_effort": "low"})
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| 65 |
"qwen": {"prefix": "", "image": "<|vision_start|><|image_pad|><|vision_end|>", "end_think": "</think>",
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"after_think": "\n\n", "strategy": "single", "threshold": 0.8, "mix": 0.5, "thinking": "off"},
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| 67 |
"gemma": {"prefix": "<bos>", "image": "<|image|>", "end_think": "<channel|>", "after_think": "",
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| 68 |
+
"strategy": "tournament", "threshold": 0.8, "mix": 0.5, "thinking": "off"},
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}
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# Read the full distribution: override generation_config defaults (top_k/top_p) that would truncate processed logprobs.
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READ = dict(max_tokens=1, temperature=1.0, top_p=1.0, top_k=0, min_p=0.0, repetition_penalty=1.0,
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