metadata
license: apache-2.0
base_model: Cloudflare/clef
pipeline_tag: text-generation
library_name: transformers
tags:
- bf16
- clef
- full-precision
- transformers
- vllm
- xinference
clef-bf16 (bf16)
Full-precision (bf16) PyTorch port of Cloudflare/clef, for vLLM / TGI / Xinference / transformers users who don't want the GGUF quant.
- Format:
pytorch_model.bin(bf16). safetensors conversion in progress. - Architecture: identical to the original — drop-in for
AutoModelForCausalLM.from_pretrained("rty813/clef-bf16"). - Also loads in vLLM (
vllm serve rty813/clef-bf16) and Xinference (search "clef").
Original model card:
license: apache-2.0 library_name: transformers pipeline_tag: image-text-to-text base_model: Qwen/Qwen3.8-27B base_model_relation: finetune tags: - clef - cloudflare - systemone - decision-model - qwen3.8 - post-train - image-text-to-typed-output - multimodal - structured-output - classification - custom-code
Clef
- Announcement: Clef decision models on the Cloudflare blog
- Decision Index leaderboard: clef-evals.workers-ai-mle.workers.dev
Clef is a 27B multimodal model that turns a state and a schema of typed questions into decisions. It reads the state as text, JSON, images, or video, and returns a probability for every allowed option of every question in a single forward pass. There is no free-form text generation and no output parsing.
The Clef API is fully compatible with Jev and SystemOne.
Clef is post-trained from Qwen/Qwen3.8-27B. See Clef-Flash for the smaller, faster variant.
Model
- Backbone: Qwen/Qwen3.8-27B with its vision encoder, stored as standard sharded safetensors.
- Joint schema head: a small transformer head that reads the backbone's final hidden states, routes evidence from the state to each question, and scores all options of all questions jointly.
- Output: one logit per allowed option for each question. Apply a softmax per