Zero-Shot Classification
Transformers
Safetensors
English
qwen3_5
feature-extraction
jeff
decision-model
calibration
system-1
local
Instructions to use mstrasser/jeff-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mstrasser/jeff-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="mstrasser/jeff-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("mstrasser/jeff-base") model = AutoModel.from_pretrained("mstrasser/jeff-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Card: Qwen3.5-0.8B licence notice for the v1.3 release
Browse files
README.md
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@@ -25,8 +25,7 @@ Catalogue, results and docs: **[jeffhub.ai](https://jeffhub.ai)**.
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**Jeff v1.3 is a change in direction. Zero-shot on everything is no longer the goal: the base is always meant to be used
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with an adapter.** The base is the foundation the adapters are trained on, and an adapter is where Jeff becomes good at
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a task. If you want zero-shot use without an adapter, use Jeff v1.2
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([mstrasser/Jeff-Qwen3.5-0.8B](https://huggingface.co/mstrasser/Jeff-Qwen3.5-0.8B), revision `v1.2`).
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## The base: live-last
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Weights: **Apache-2.0**, as for v1.2. The model is a fine-tune of Qwen3.5-0.8B by the Qwen team (Alibaba Cloud).
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Training data: the same data as the v1.2 base, which mixes public data sets under various licences, some of them
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share-alike (CC BY-SA), with code-built and synthetic questions. The training data is not released. The v1.2 data
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sources and their licences are listed in
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- Results: [jeffhub.ai/results](https://jeffhub.ai/results)
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- Code and server: [github.com/firelex/jeff](https://github.com/firelex/jeff)
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- GGUF: [mstrasser/jeff-base-gguf](https://huggingface.co/mstrasser/jeff-base-gguf)
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- Jeff v1.2 (zero-shot): [mstrasser/Jeff-Qwen3.5-0.8B](https://huggingface.co/mstrasser/Jeff-Qwen3.5-0.8B), revision `v1.2`
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Jeff is an independent project. It uses the same request format as Jev but is not affiliated with or endorsed by
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TypeSafe, the makers of Jev.
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**Jeff v1.3 is a change in direction. Zero-shot on everything is no longer the goal: the base is always meant to be used
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with an adapter.** The base is the foundation the adapters are trained on, and an adapter is where Jeff becomes good at
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a task. If you want zero-shot use without an adapter, use Jeff v1.2.
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## The base: live-last
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Weights: **Apache-2.0**, as for v1.2. The model is a fine-tune of Qwen3.5-0.8B by the Qwen team (Alibaba Cloud).
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**Qwen3.5-0.8B notice:** these weights were modified from [Qwen3.5-0.8B](https://huggingface.co/Qwen/Qwen3.5-0.8B) by the Jeff project. Qwen3.5-0.8B is Copyright 2026 Alibaba Cloud and licensed under the Apache License, Version 2.0; a copy of that licence is in [`LICENSE`](LICENSE).
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Training data: the same data as the v1.2 base, which mixes public data sets under various licences, some of them
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share-alike (CC BY-SA), with code-built and synthetic questions. The training data is not released. The v1.2 data
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sources and their licences are listed in
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- Results: [jeffhub.ai/results](https://jeffhub.ai/results)
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- Code and server: [github.com/firelex/jeff](https://github.com/firelex/jeff)
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- GGUF: [mstrasser/jeff-base-gguf](https://huggingface.co/mstrasser/jeff-base-gguf)
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Jeff is an independent project. It uses the same request format as Jev but is not affiliated with or endorsed by
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TypeSafe, the makers of Jev.
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