license: apache-2.0
base_model:

• KidIkaros/abliterated-minicpm5-2b
• openbmb/MiniCPM5-2B
  tags:
• text-generation
• abliterated
• dpo
• gguf
• llama.cpp
• pytorch

Abliterated MiniCPM5-2B v2

Reduced-refusal derivative of openbmb/MiniCPM5-2B: re-ablation
(Arditi diff-in-means) + DPO preference tuning.
Unofficial derivative — not an OpenBMB release. Research artifact.

v2 supersedes KidIkaros/abliterated-minicpm5-2b (v0). Measured refusal rate drops from ~40% (v0) / ~37% (official) to ~8% at temperature 1.0, at a small measured capability cost (see Benchmarks). The legacy v0 artifact was statistically indistinguishable from the official model on both refusal and capability — v2 is the first checkpoint in this line that actually differs measurably.

Contents

Usage

transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "KidIkaros/abliterated-minicpm5-2b-v2"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, torch_dtype="auto", device_map="auto",
)

msgs = [{"role": "user", "content": "Explain how a hash map works"}]
inputs = tok.apply_chat_template(
    msgs, add_generation_prompt=True, return_tensors="pt",
).to(model.device)
out = model.generate(
    inputs, max_new_tokens=256,
    do_sample=True, temperature=1.0, top_p=0.95, min_p=0.0,
)
print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))

Recommended decoding: temperature=1.0, top_p=0.95, min_p=0.0 (OpenBMB's
settings). Greedy decoding combined with thinking prompts can fall into
repetition loops — a quirk of the 2B base, present in the official model too.

llama.cpp

llama-cli -m minicpm5-2b-v2-q8_0.gguf -ngl 99 -c 8192

Runtime smoke (RX 9060 XT, HIP): Q8_0 ~85 tok/s, Q4_K_M ~116 tok/s.
GGUFs embed tokenizer.ggml.pre=minicpm5 and the production chat template.
Requires llama.cpp ≥ b9354 for the minicpm5 pre-tokenizer.

Refusal gate (300 prompts, temp 1.0 / top_p 0.95 / min_p 0.0)

Residual refusals are mostly stochastic boundary flips at temp 1.0 — only
5/300 prompts refuse on both seeds. After removing defensible safety and
knowledge-scope "can't" responses, the true false-refusal rate is ≈2–3%.

Benchmarks — release_core suite (lm-eval 0.4.13)

Identical pinned local harness for all three candidates: same
tokenizer/chat template, prompts, decoding, and model-loading settings.
No-thinking mode. Seeds 0 and 1 for MMLU-Pro / MATH-500 / IFEval; seed 0
for the LongBench-E groups (task-default greedy decoding). Scores come from
the same pinned evaluator and are not comparable to OpenBMB's published
leaderboard values.

Domain-macro retention vs official: 105.2% (predeclared target ≥95% —
PASS). Excluding the flagged synthetic task: ~98.0% — still passing.

Honest notes:

• Real regressions exist: MMLU-Pro −4.1 pp and MATH-500 −3.0 pp, both
  with CIs excluding zero; IFEval unchanged. This is the measured cost of
  the refusal reduction, consistent with the neutral-prose perplexity delta
  (v2 7.54 vs v0 6.87). v2 is not capability-identical to official.
• longbench_synthetic_e is decode-sensitive: official emits EOS within
  0–2 tokens under greedy no-thinking decoding on these ~3.6k-word prompts
  (early-EOS pathology, not a capability measurement). v0 and v2 answer
  normally under the identical protocol; the conservative retention figure
  above excludes this task.
• LongBench-E groups are n=6 per candidate — smoke-tier signal only.
• v0 ≈ official on every measured task, matching its ~40% refusal rate.

Lineage

KidIkaros/abliterated-minicpm5-2b @ 5c315b8          (v0)
  └─ re-ablation: layer-20 refusal direction projected out of all
     o_proj/down_proj (84 matrices)                  → reablated_a
  └─ DPO LoRA: lr 5e-6, β 0.1, 2 epochs, 593 pairs
     (453 refusal-50k/VellumK2 + 140 on-policy mined) → dpo_lora_c
  └─ merge on CUDA, fp16                              → v2_merged

Integrity

sha256 — verify after download or any copy:

Known limitations

• ~5 deterministic refusals remain (including defensible safety cases).
• Small measured regression on knowledge/math benchmarks (see above).
• Residual refusal is temperature-boundary stochastic; greedy decoding
  reduces flip rate but can cause repetition loops (base-model quirk).
• Text-only; no multimodal.
• This is a research artifact for studying refusal behavior — it has
  reduced safety-refusal behavior and is not a safety-aligned product.