Configuration Parsing Warning:In adapter_config.json: "peft.task_type" must be a string

jeff-adapter-emotion

Emotion in short comments. Picks the strongest of 27 emotions, or neutral, in a short comment or message.

A LoRA adapter for jeff-base v1.3, a small open decision model (a fine-tune of Qwen3.5-0.8B). You send a situation (the state) and questions with named options; Jeff returns a calibrated probability for every option from one forward pass, with no generated text to parse. One Jeff server loads the base once and any number of adapters beside it; each request picks an adapter by name ("model": "emotion").

Adapter page, with the full data card: jeffhub.ai/adapters/emotion.

Results

On this adapter's held-out test set, never trained on, scored three ways on the same rows: the untrained model Jeff is built from, the Jeff v1.3 base alone, and the base with this adapter. Every question has 28 options. As of 2026-10-05. All adapters

Test set	Test rows	Qwen3.5-0.8B untrained	Jeff base v1.3 alone	Jeff base v1.3 + adapter
`test`	5,408	12.6% · 0.045	25.3% · 0.080	60.5% · 0.018

Each cell: accuracy · calibration error (ECE; lower is better, 0 is perfect).

With llama.cpp (GGUF)

The same test, through llama.cpp: the base GGUF (mstrasser/jeff-base-gguf) plus this adapter's LoRA GGUF (mstrasser/jeff-adapter-emotion-gguf), with the temperature refitted for each format. Running Jeff with llama.cpp

Test set	Full precision	Q8_0	Q4_K_M
`test`	60.5% · 0.018	60.1% · 0.012	60.2% · 0.017

Not measured yet for v1.3: calibration charts, the commonest confusions and accuracy per answer.

Source of these numbers: results/sources/v1.3/retrained-adapters.table.json in the JeffHub repository, also collected in jeffhub-v1.3.json.

When to use it

You want a finer reading of feeling than positive or negative, for example gratitude, confusion or disappointment.
Your texts are short, informal English comments or messages.
You are happy to read the result as a spread of probabilities. Many comments carry more than one emotion.

When not to use it

Your texts are long, formal or not in English. All training text is English Reddit comments.
You need a clinical or safety judgement, such as risk of self-harm. The adapter only names emotions.
You need every emotion in a text listed separately. The question asks for the one expressed most.

How to use it

The adapter runs with Jeff's server, on the main branch of firelex/jeff, on the jeff-base v1.3 base.

git clone https://github.com/firelex/jeff && cd jeff
uv sync --no-default-groups --extra lora          # add --extra cuda on NVIDIA GPUs, --extra mac on Apple silicon
uv run --no-default-groups hf download mstrasser/jeff-base --revision v1.3 --local-dir checkpoints/jeff-base
uv run --no-default-groups hf download mstrasser/jeff-adapter-emotion --revision v1.3 --local-dir adapters/emotion
JEFF_CHECKPOINT=checkpoints/jeff-base JEFF_ADAPTERS=adapters/ PORT=8765 \
  uv run --no-default-groups jeff-serve          # on a Mac, add JEFF_BACKEND=mlx

Every folder in adapters/ is served under its folder name; add or replace adapters while the server runs with curl -X POST http://localhost:8765/v1/adapters/reload. Each adapter records the exact base it was trained on, and the server refuses an adapter trained on a different one, so this adapter loads only on jeff-base v1.3 (a v1.2 adapter does not load on v1.3). For llama.cpp, use mstrasser/jeff-adapter-emotion-gguf.

Request format

State (the situation), in this order:

Key	Changes per request	What it holds
`source`	no	One short phrase on where the text comes from. In training this was always "Reddit comment ([NAME] and [RELIGION] replace removed names)".
`comment`	yes	The comment or message to read.

Questions:

emotion (choice): Which emotion the comment expresses most strongly, or neutral if it expresses no particular emotion. Options: 28 options: 27 emotions and neutral, keyed by their GoEmotions names (admiration, amusement, anger and so on), each with a one-line description. The full list is GOEMOTIONS in descriptions.py in the source.

Rules:

Use the option keys and descriptions from descriptions.py; the adapter was trained on them.
Comments with several gold emotions were trained with the probability spread evenly over them, so a split answer is expected, not a fault.
Use the instructions below word for word; the adapter was trained mostly on them.

General rules for every request: the request format guide.

Example

The request below is also in this repository as example.json.

{
  "model": "emotion",
  "state": {
    "source": "Reddit comment ([NAME] and [RELIGION] replace removed names)",
    "comment": "Thanks so much for the tip, I had no idea the library lent out tools. Saved me a fortune."
  },
  "questions": {
    "emotion": {
      "type": "choice",
      "instructions": "Which emotion does the comment express most? Choose the emotion that is strongest in the comment, or neutral if it expresses no particular emotion.",
      "criteria": {
        "gratitude": "Gratitude: feeling thankful or appreciative.",
        "joy": "Joy: a feeling of pleasure and happiness.",
        "surprise": "Surprise: being astonished or startled by something unexpected.",
        "realization": "Realization: becoming aware of something.",
        "admiration": "Admiration: finding something impressive or worthy of respect.",
        "relief": "Relief: reassurance and relaxation after anxiety or distress ends.",
        "annoyance": "Annoyance: mild anger or irritation.",
        "confusion": "Confusion: lack of understanding or uncertainty.",
        "sadness": "Sadness: feeling sorrow or unhappiness.",
        "neutral": "Neutral: no particular emotion is expressed."
      }
    }
  }
}

curl -s localhost:8765/v1/systemone -H 'content-type: application/json' -d @adapters/emotion/example.json

The answer holds a probability for each option of each question. A recorded response from the v1.3 adapter is not published yet.

Files

adapter_model.safetensors, adapter_config.json: the LoRA weights (PEFT format);
readout.safetensors: the adapter's own readout over the answer codes;
decision_config.json: answer codes, temperature, prompt layout and the checksum of the base it was trained on;
test.jsonl: the held-out test set the results below were measured on;
calibration.jsonl: the calibration rows the adapter's temperature was fitted on;
example.json: the example request above.

adapter_config.json and decision_config.json name the base as mstrasser/jeff-base, revision v1.3; the server checks the base by the checksum of its weights.

Training


Base	mstrasser/jeff-base, revision v1.3 (a fine-tune of Qwen3.5-0.8B)
Prompt layout	live-last: the fixed part of the request first, the changing state field last
Training code	The `git_commit` recorded in `decision_config.json` is the training machine's copy and was not published. It builds exactly the same prompt as `main` of firelex/jeff (from commit `6d0d7da`) for a text state and for an object with at least one field; the format is in docs/v1.3-request-format.md
Run	`0.8b-emotion-20261003-0149`, final checkpoint
Adapter files	41.5 MB (adapter_model.safetensors and readout.safetensors)
LoRA GGUF for llama.cpp	mstrasser/jeff-adapter-emotion-gguf

1.3.0 (2026-10-03): Trained on Jeff v1.3 with the live-last prompt layout (LoRA rank 16, one epoch, about 10% of the base model's own training data mixed in).

Data card

Report attached. The shortcut report and data card are included and pass the JeffHub checks; the numbers are the maintainers’ own. What the levels mean

Test set: included in this repository as test.jsonl, so anyone can check the numbers
Calibration rows: included in this repository as calibration.jsonl, the rows its threshold is chosen on
QA report, sanitised: the data-quality checks run before training

How the test set was held out. The official GoEmotions test split (simplified configuration); never trained on.

Training data. Built from public data sets, listed under Data and licence.

The source data sets are public (listed under Data and licence). A script to rebuild our rows from them will follow.

Training mixed in a replay sample of the Jeff base model's own training data: 4,607 rows, about 10% on top of the adapter's 46,067 (inherited from the v1.2 recipe as a precaution; its effect has not been measured).

Data and licence

Adapter licence: Apache-2.0.

Qwen3.5-0.8B notice: these weights were modified from Qwen3.5-0.8B by the Jeff project: jeff-base is a fine-tune of Qwen3.5-0.8B, and this adapter was trained on top of it. 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.

It was trained on:

GoEmotions, simplified configuration. Licence: Apache-2.0 (open licence)

English Reddit comments with names removed. Option descriptions were written by hand from the label names.

Limitations

Tied to jeff-base v1.3. It will not load on any other base or version; the server checks the base weights' checksum.
Jeff chooses between the options you give it. It does not write text or reason in several steps.
Calibration was fitted on this adapter's own calibration rows. On very different data, check it again.
Everything listed under When not to use it above.

Model tree for mstrasser/jeff-adapter-emotion

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Finetuned

mstrasser/jeff-base

Adapter

(30)

this model

mstrasser
/

jeff-adapter-emotion