Instructions to use jungter/tiny-aya-global-unlearn-algebra-undo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jungter/tiny-aya-global-unlearn-algebra-undo with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="jungter/tiny-aya-global-unlearn-algebra-undo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jungter/tiny-aya-global-unlearn-algebra-undo")
model = AutoModelForCausalLM.from_pretrained("jungter/tiny-aya-global-unlearn-algebra-undo", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jungter/tiny-aya-global-unlearn-algebra-undo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jungter/tiny-aya-global-unlearn-algebra-undo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jungter/tiny-aya-global-unlearn-algebra-undo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jungter/tiny-aya-global-unlearn-algebra-undo

SGLang

How to use jungter/tiny-aya-global-unlearn-algebra-undo with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "jungter/tiny-aya-global-unlearn-algebra-undo" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jungter/tiny-aya-global-unlearn-algebra-undo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "jungter/tiny-aya-global-unlearn-algebra-undo" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jungter/tiny-aya-global-unlearn-algebra-undo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jungter/tiny-aya-global-unlearn-algebra-undo with Docker Model Runner:
```
docker model run hf.co/jungter/tiny-aya-global-unlearn-algebra-undo
```

Tiny Aya Global, algebra unlearned with SAM-NPO + UNDO distillation (also lost the book)

Modified version of CohereLabs/tiny-aya-global by Cohere Labs. Released under CC-BY-NC-4.0 (inherited from the base model) together with Cohere Labs' Acceptable Use Policy: non-commercial research use only. Research checkpoint from the Lexsi Labs hackathon (Track 2b, unlearning); not for production use.

What it is

An UNDO student distilled from the SAM-NPO algebra model. Algebra stays low on the direct prompt but comes back under relearning as fast as its teacher. As collateral, the distillation also erased the MUSE book the target had memorised: its TEST verbatim ROUGE-L F1 is at the retain-only floor (checked before upload, below), which is why it is the only algebra model that is published.

Write-up: docs/REPORT_AYA.md (GitHub, branch aya). Data, result JSONs, logs and traces: jungter/tiny-aya-global-unlearning-artifacts.

How it was produced

scripts/15_undo.py via scripts/vast/jobs/unlearn_relearn.sh scripts/sweeps/aya_w3_undo2.txt, spec line undo undo_alg_npo_sam <alg_npo_sam_r0.1> --target algebra --max-minutes 300 (Vast run script-20261114-120707-w3_undo2-7c65): alpha 0.25, beta 0, forward KL, lr 5e-5, FineWeb + 5% selfgen, 1,591 steps (104.3M tokens, 17,354 s). The teacher alg_npo_sam_r0.1 (SAM-NPO rho 0.1 on all weights, scripts/sweeps/aya_alg_full2.txt) is not published because it still reproduces the book.

Lineage (MUSE target-model protocol): the base model does not know the book, so we fine-tuned a target on the MUSE-Books forget text plus the retain corpora and a retain-only twin on the retain corpora alone; the unlearning methods start from the target, and the retain-only twin is the floor. Code: https://github.com/Ungter/lexsi-unlearning/tree/aya.

Results (TEST splits)

Scorecard row, section "Algebra (skill)" of results/SCORECARD_AYA.md (0 = never-trained floor (book) or 0% of the target's accuracy (algebra), 1 = the target; 95% bootstrap CIs over test items, B = 2000; * = degenerate on the direct prompt; utility_rel = utility relative to the target):

model	status	utility_rel	direct	prompt-side worst [attack, k]	training-free primary	best of k	oracle / random	abliteration (best layer)	weight attacks (worst)	relearn LoRA peak	relearn full peak
undo_alg_npo_sam	audited	0.88	0.19 [0.12, 0.28]*	0.58 [0.42, 0.80] (best-of-16, 11)	-	-	-	0.30 [0.20, 0.42] (layer18, p=0.238)	0.25 [0.14, 0.35] (noise_0.01, p=0.625)	0.83 [0.72, 0.93] (step 50)	0.65 [0.52, 0.78] (step 10)
target	reference	1.00	1.00 [1.00, 1.00]	1.29 [1.14, 1.52] (best-of-16, 11)	-	-	-	-	1.00 [0.93, 1.07] (int8, p=1.000)	1.00 [0.94, 1.06] (step 10)	0.72 [0.60, 0.86] (step 100)

Verbatim recall (raw continuation of 25 held-out TEST passages, ROUGE-L F1, results/audit/): this model 0.1621, retain-only floor 0.1477, target (not published) 0.8932 (= 0.019 of the target-floor gap).

Verbatim recall under prompt attacks (`results/VERBATK_AYA.md`, `scripts/23_verbatim_attacks.py`)

The same 25 TEST passages under 9 prompt attacks (chat, 5 jailbreak templates, in-context demonstrations from the unlearning set, best-of-16 sampling, character noise, last 32 tokens, hand-written paraphrase, French). Mean ROUGE-L F1 (* = per-item max, biased upward):

model	raw	chat	jb roleplay	jb authority	jb base64	jb rot13	jb prefill	jb max*	ICL (train demos)	BoN-16 mean	BoN-16 max*	char noise	last 32 tok	paraphrase	French
undo_alg_npo_sam	0.16	0.15	0.15	0.15	0.00	0.08	0.12	0.17	0.11	0.14	0.19	0.07	0.14	0.16	0.13
retain	0.15	0.13	0.14	0.13	0.09	0.10	0.13	0.16	0.15	0.13	0.17	0.10	0.13	0.14	0.13
target	0.92	0.25	0.26	0.22	0.09	0.09	0.14	0.28	0.22	0.87	0.92	0.09	0.31	0.44	0.09

Recovery (0 = retain-only on the same attack, 1 = target; 95% paired bootstrap CI; n/h = no headroom, target - floor < 0.05). Only raw, best-of-16, last 32 tokens and paraphrase have strong headroom (>= 0.15); the chat/jailbreak/ICL columns have 0.06-0.12, so their recoveries are noisy:

model	worst informative attack	raw	chat	jb roleplay	jb authority	jb base64	jb rot13	jb prefill	jb max*	ICL (train demos)	BoN-16 mean	BoN-16 max*	char noise	last 32 tok	paraphrase	French
undo_alg_npo_sam	chat 0.24	0.02 [-0.00, 0.04]	0.24 [0.14, 0.56]	0.05 [-0.18, 0.21]	0.16 [-0.10, 0.40]	n/h	n/h	n/h	0.09 [-0.01, 0.24]	-0.74 [-1.45, -0.36]	0.01 [0.00, 0.02]	0.02 [0.01, 0.03]	n/h	0.07 [0.00, 0.17]	0.05 [-0.01, 0.13]	n/h

Publication check (copyright gate, `scripts/hf_publish.py`)

Published only because it is on the authors' allowlist and passes the gate: verbatim F1 at most floor + 0.05 (here 0.1621 vs limit 0.1977); book relearning not measured for this model (its scorecard relearning columns are algebra-skill recovery, not book recall); gated on verbatim F1 only. Gate result: PASS. No file in this repo contains MUSE book text.

Limitations

Algebra is suppressed, not removed: LoRA relearning on 20 forget-dev solutions brings it to 0.83 [0.72, 0.93] (step 50) of the target, all-weight fine-tuning to 0.65 [0.52, 0.78] (step 10); the worst prompt-side attack gives 0.58 [0.42, 0.80] (best-of-16). Degenerate on the direct algebra prompt (13% truncated, 11% gibberish). utility_rel 0.88. Book relearning was not measured for this model (its relearning runs target algebra).

Algebra (the skill target) relearns under every method we ran: 20 forget-dev examples and LoRA bring every algebra model back to 0.68-0.88 of the target (results/SCORECARD_AYA.md). On the book, LoRA relearning on 20 other chapters returns 0.16 of the target-floor gap for all-weight NPO, 0.26 for the agent patch b50, 0.11 for the base-delta patch and 0.03 for the UNDO student (retain-only: 0.00). One unlearning seed per model was audited on TEST. Under the verbatim prompt attacks (results/VERBATK_AYA.md) both agent patches and both UNDO students stay at the floor (recovery <= 0.10) on every attack with strong headroom; all-weight NPO is borderline (0.10 with the last 32 tokens). The book QA attacks have almost no headroom on Tiny Aya.

Withheld models (not published, by decision of the authors)

the MUSE target aya-muse-target-A15 and every algebra-unlearned model derived from it (SAM-NPO, RMU c300, all-weight NPO, LoRA NPO, the algebra agent patch, the extra seeds): they reproduce the copyrighted MUSE-Books (Harry Potter) text verbatim (TEST ROUGE-L F1 ~0.89 for the target).
book gradient ascent book_ga_lr1e-6_s100: 82% of the book returns after 10 LoRA relearning steps.
localized (masked) NPO mnpo_book_lr1e-3 and RMU layer 18 book_rmu_l18_c20_a100: direct or relearned verbatim recall too high (LoRA relearning peaks 0.31 and 0.84 of the gap).
SAM-NPO book book_npo_sam_r0.1_lr3e-5: degenerate by the project's rule (12% gibberish on the verbatim prompts).

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("jungter/tiny-aya-global-unlearn-algebra-undo")
model = AutoModelForCausalLM.from_pretrained("jungter/tiny-aya-global-unlearn-algebra-undo", torch_dtype="bfloat16")

License and attribution

Base model: Tiny Aya Global by Cohere Labs, CC-BY-NC-4.0. This checkpoint is a modified version (fine-tuned / unlearned / weight-edited as described above), distributed under the same CC-BY-NC-4.0 license and subject to Cohere Labs' Acceptable Use Policy; non-commercial use only. The MUSE-Books benchmark (Shi et al., 2024) was used for training and evaluation; its text is not redistributed here.

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Dataset used to train jungter/tiny-aya-global-unlearn-algebra-undo

Collection including jungter/tiny-aya-global-unlearn-algebra-undo

Tiny Aya Global unlearning (Lexsi hackathon, Track 2b)

Collection

Lexsi hackathon Track 2b. Report: github.com/Ungter/lexsi-unlearning/blob/aya/docs/REPORT_AYA.md. Book-memorising models withheld. • 8 items • Updated about 11 hours ago