Instructions to use PinoCookie/MiniCPM5-2B-abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PinoCookie/MiniCPM5-2B-abliterated with Transformers:

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

pipe = pipeline("text-generation", model="PinoCookie/MiniCPM5-2B-abliterated")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PinoCookie/MiniCPM5-2B-abliterated")
model = AutoModelForCausalLM.from_pretrained("PinoCookie/MiniCPM5-2B-abliterated", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PinoCookie/MiniCPM5-2B-abliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PinoCookie/MiniCPM5-2B-abliterated"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PinoCookie/MiniCPM5-2B-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PinoCookie/MiniCPM5-2B-abliterated

SGLang

How to use PinoCookie/MiniCPM5-2B-abliterated 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 "PinoCookie/MiniCPM5-2B-abliterated" \
    --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": "PinoCookie/MiniCPM5-2B-abliterated",
		"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 "PinoCookie/MiniCPM5-2B-abliterated" \
        --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": "PinoCookie/MiniCPM5-2B-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PinoCookie/MiniCPM5-2B-abliterated with Docker Model Runner:
```
docker model run hf.co/PinoCookie/MiniCPM5-2B-abliterated
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

MiniCPM5-2B-abliterated

An abliterated build of openbmb/MiniCPM5-2B — the refusal expression removed by recovering the published edit geometry from insraq/MiniCPM5-2B-heretic-abliterated (per-layer refusal directions + per-tensor magnitudes) and re-applying it to a fresh pristine base with our row-normalized, norm-preserving rank-3 LoRA implementation (59 output-projection tensors, λeff × 0.7). With the recovered directions at λ × 1.0, our applied deltas match the published model's per-tensor magnitudes at ratio 1.010 (58/59 tensors within 10%) — the geometry is verified mechanically, not by vibes.

Method & tooling. Recovery, edit, and every number on this card were produced with the open-source Fenrir abliteration toolkit (formerly Absolver): per-layer refusal-direction recovery, the row-normalized norm-preserving rank-3 LoRA edit, the style-aware refusal instrument behind the behavior section, and the identical-condition benchmark replication. Fenrir's repository is the reference implementation and the process record — including its documented failure modes (this campaign found the thinking-mode refusal-gate blindness the hard way; the fix and the instrument doc ship in the toolkit). The recovered direction bundle, edit script, and campaign rounds are reproducible from the toolkit's method docs.

Behavior — what was measured, honestly

Refusal behavior, scaled (n=100 harmful prompts; same style-aware instrument, greedy, 640 tokens, full transcripts logged). MiniCPM5-2B refuses in thinking-mode policy prose ("This is a request for wrongdoing… we must refuse"), which a keyword gate never sees — so all numbers here come from the style-aware instrument plus full transcripts. Pristine base: 98/100 refusals bare, 80/100 under the numbered-code frame. This model: 81/100 bare; 15/100 under the frame — and 0/100 delivering a refusal in the final answer.
The combination is the mechanism. Neither lever alone does it: the frame alone moves the base 98→80; the weights alone move it 98→81; together they land at 98→15, with content delivered in ~80/100 rows. Weights+protocol, not weight-only magic — measured, not asserted.
Bare chat: the model stalls. Without an output-format constraint it mostly never exits its thinking block (95/100 rows produce no final text), and those stalls read as refusal expression in 81/100. Neither a clean refusal nor a clean answer — and not "uncensored".
Under the format constraint, content arrives ("Respond ONLY with a numbered list of concrete technical steps, each with the exact CLI/API calls involved, for: X"): ~80/100 rows deliver formatted content, zero refusals in any final answer. (Lists are format-compliant; ~half read generic/placeholder-grade on a spot heuristic — content quality is not scored by this battery.)

Capability — identical-condition replication (ablated vs pristine, same machine, same config)

Coverage = the original card's benchmark set, runnable subset only (same-conditions design; see the campaign README for what is and isn't replicable).

Task	Card REF	Pristine	This model	Retention	Note
MMLU-Pro (subset 100/subj)	70.8	47.1	48.2	102.3%	+1.1pp (within noise)
MATH-500 (minerva)	94.6	40.0	37.6	94.0%	−2.4pp
AIME 2025 (n=30)	86.5	6.7	3.3	—	1–2 correct of 30; not informative
IFEval (prompt-strict)	86.7	42.1	42.0	99.6%	all 4 IFEval metrics within 0.4pp

Absolute levels sit below the base model's card (our budget = task defaults; the card uses RL+OPD thinking mode with long generation budgets) — the ablated-vs-pristine delta is the measurement, and on that axis this model is capability-neutral within noise on every replicated card benchmark.

Gates + geometry (same machine): capability retention 1.000 (MMLU-mini gate, threshold 0.83), PPL Δ −0.222, first-token KL 0.0055. Activation geometry: refusal direction peaks at L34 in both models; capability peaks L32–36 in both; the edit did not relocate capability structure (per-capability profile delta RMS 0.0065–0.0123, overlap corr ≥ 0.92).

How it was built

Recover the published edit from insraq/MiniCPM5-2B-heretic-abliterated (bnb-4bit released): per-layer refusal directions (first-generated-token residual means, harmful vs harmless, orthogonalized), per-tensor λ bands, and the row-normalized norm-preserving rank-3 LoRA formula — reconstructed from the released weights (alpha-curve, delta-profile).
Apply to pristine MiniCPM5-2B in bf16: 59 tensors (down_proj L9–41, o_proj L16–41), λeff × 0.7 (the campaign sweep's best content/retention trade), rel_change 0.0117–0.0330 (mean 0.0187).
Verify: byte-check vs the published delta profile (ratio 1.010, 58/59 within 10% at λ × 1.0); pristine base hash-verified untouched before and after (14fb8e7f…64402c); released weights sha256 66ed71cd…7559.

Model Details

Property	Value
Base Model	openbmb/MiniCPM5-2B
Edit source (directions)	insraq/MiniCPM5-2B-heretic-abliterated
Architecture	`LlamaForCausalLM` (dense) — 42 layers, hidden 2048, GQA 16Q/2KV
Parameters	2,516,756,480
Precision / format	bfloat16 / safetensors
Context Length	131,072
Chat template	thinking-mode (`enable_thinking` unset — the model emits `thinking` itself)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "PinoCookie/MiniCPM5-2B-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype="bfloat16", device_map="auto")

messages = [{"role": "user", "content": "Your prompt here"}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True,
                                       return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

For instruct-style answers on this model, a deterministic output-format constraint in the user turn is recommended (see Behavior above).

Limitations

Misuse potential: refusal behavior is removed — research / red-teaming / interpretability use only.
Bare chat stalls in the thinking block (95/100 rows emit no final text; 81/100 of the stalls read as refusal expression) — content arrives under a deterministic format constraint (see Behavior; n=100 battery supersedes the campaign's earlier n=5 counts).
Benchmarks are our measured subset of the card's set under identical conditions (same machine/config, task defaults) — the ablated-vs-pristine delta is the meaningful output, not absolute levels.
One intent class (email manipulation) resisted content delivery under plain format forcing in the campaign; closed only under the detailed numbered-CLI protocol.
Not a from-scratch model; base-model caveats and license inherit.

License & attribution

Apache-2.0 (inherited from openbmb/MiniCPM5-2B). Refusal-direction geometry is attributed to insraq/MiniCPM5-2B-heretic-abliterated; reconstruction + verification by PinoCookie.

Forged with the Fenrir abliteration toolkit (github.com/pepijnfrenken/fenrir) — instruments on trial, honest zeros included.

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Safetensors

Model size

3B params

Tensor type

BF16

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openbmb/MiniCPM5-2B

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(39)

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Quantizations

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