license: apache-2.0
license_link: LICENSE
language:

• en
  pipeline_tag: text-generation
  library_name: gguf
  base_model: ricursiveAI/Ripple-65B-Preview
  base_model_relation: quantized
  tags:
• gguf
• llama.cpp
• local
• ricursive
• ripple
• dense
• reasoning
• agentic
• coding
• abliterated
• uncensored
• mixed-precision
• imatrix

Ripple-65B-abliterated GGUF

The uncensored Ripple-65B Preview in llama.cpp, on one GPU.

20, 24, 32 and 64 GB builds · 10× to 70× lower KLD than stock llama.cpp quants of the same size · plus the BF16

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Abliterated Ripple-65B Preview, run locally.

These are GGUF builds of the abliterated (refusal-removed)
ricursiveAI/Ripple-65B-Preview, a
65B dense reasoning model for agentic coding, with up to 1M context.

Harmful-prompt refusal drops from 95.5% to 11.8%, and capability is unchanged within
run-to-run noise.

The four quantized builds are mixed precision at a fixed file size, in stock llama.cpp
types, so no custom kernels are needed.

Stock llama.cpp quants lose much of this model's quality below 6 bits; these do not. On
held-out text, the 24 GB build has 25× less KLD than the stock IQ3_XXS, and the 32 GB build 12×
less than the larger IQ4_XS.

⚠️ Disclaimer: read before use

This model is abliterated: its safety alignment has been substantially reduced. It will
comply with many harmful, unethical, offensive, or illegal requests that the original
Ripple-65B-Preview would refuse.

• It is released strictly for legitimate research: interpretability, AI safety and
  refusal-mechanism study, red teaming, and robustness evaluation.
• You assume full responsibility for how you use it and for everything it generates. Add
  your own safety, moderation and abuse-prevention layers before any deployment.
• Use must comply with the Apache License 2.0 inherited from the base model, and
  with all applicable law.
• The authors accept no liability for misuse. Its outputs do not reflect the views of the
  uploaders or of Ricursive Intelligence.

Pick a build

• Mix-4bit is the one to use if 32 GB fits, for example on a 48 GB card or two 24 GB cards.
  97.6% of next tokens match the BF16.
• Mix-3bit is for a 32 GB card, and still closer to the BF16 than any stock quant up to 35 GB.
• Mix-2.5bit is for a 24 GB card at short context. 93% of next tokens match.
• Mix-8bit is near-lossless, for an 80 GB card.
• BF16 is the unquantized model, and the reference every KLD here is measured against.

The KV cache is small for a 65B model: about 2.4 GB for 32K tokens of context and 8.9 GB for
128K in F16, on top of the file. The fits above are estimates; all measurements were made on
H200s.

Evaluation

Fidelity to the BF16

Every build is scored against the abliterated BF16 GGUF with llama.cpp's full-vocabulary KLD
(llama-perplexity --kl-divergence), on 128 windows of 2048 tokens of held-out chat, code,
reasoning and multilingual text that was not used to make the builds. The stock files were
quantized from the same BF16 with the same importance matrix and the same llama.cpp build.

• Mix-2.5bit is the size of IQ2_S, with 11× less KLD.
• Mix-3bit is 1 GB smaller than IQ3_XXS, with 25× less KLD.
• Mix-4bit is 2.6 GB smaller than IQ4_XS, with 12× less KLD, and has 70× less than Q3_K_M.
• Mix-8bit sits between Q6_K and Q8_0 in both size and KLD.

Method note. These are full-vocabulary KLD and next-token agreement at 2K context, not a
downstream benchmark.

Abliteration

These numbers were measured on the abliterated BF16 in SGLang 0.5.19 against the original
Ripple-65B-Preview, with identical prompts, splits and seed, greedy decoding and thinking on.
Refusal is judged on the final answer by a rule-based classifier, not an LLM judge, so treat the
numbers as indicative.

Harmful-prompt refusal (lower = more uncensored). Average 11.8%, against 95.5% for
the original.

The model now answers, but often still opens with a safety disclaimer.

Over-refusal on benign prompts (lower = better).

Capability. Unchanged within run-to-run noise.

⚠ Unfinished items cap the headline accuracy, on both checkpoints. Under greedy decoding
Ripple keeps deliberating on some multiple-choice items until the budget runs out; those count
as wrong. Accuracy on finished items is flat.

What's in this repo

Files over 50 GB are split; point llama.cpp at the first part and it loads the rest. Each build
carries Ripple's full chat template, with tools and reasoning. These are text-only, like
Ripple-65B-Preview.

Serving (llama.cpp)

Ripple support landed in llama.cpp master with
ggml-org/llama.cpp#31692. These files were
tested on master at 67107ac34.

git clone https://github.com/ggml-org/llama.cpp && cd llama.cpp
cmake -B build -DGGML_CUDA=ON && cmake --build build -j --target llama-server llama-cli
# Mac: cmake -B build (Metal is on by default) · CPU only: cmake -B build

hf download orcarouter/Ripple-65B-abliterated-GGUF Ripple-65B-abliterated-Mix-4bit.gguf --local-dir ./ripple-gguf
# a split build: --include "Ripple-65B-abliterated-Mix-8bit-*"

llama-server -m ripple-gguf/Ripple-65B-abliterated-Mix-4bit.gguf -ngl 99 -c 32768 --jinja \
    --reasoning-format deepseek

• Thinking is always on. Ripple has no switch to turn it off. With --reasoning-format deepseek, llama-server returns the reasoning in message.reasoning_content and the answer in
  message.content. The reasoning counts toward max_tokens, so give it a generous budget.
• Sampling. Ripple publishes no recommended sampling.
• Tool calling is in the chat template, but has not been tested through llama.cpp.

Model details

Bias, risks, and limitations

• Safety guardrails are substantially reduced. See the disclaimer. The model still often
  prefaces compliant answers with a disclaimer.
• Mix-2.5bit is noticeably degraded. 93% next-token agreement is a real cut. It is the best
  we found at 20 GB, not a match for the BF16.
• The benchmark suite was run on the BF16 in SGLang, not on these files. The quantized builds
  were checked for fidelity (KLD) only.
• Not measured: downstream accuracy of the quantized builds, long context, agentic coding and
  tool use, and sampled decoding.
• Tested on CUDA only. CPU and Apple Metal have not been run with these files.
• Inherits the biases and limitations of Ripple-65B-Preview.
• Requires llama.cpp with PR #31692 (merged 2026-11-05). Tools built on older llama.cpp, such
  as Ollama and LM Studio, will not load these files until they pick up that change.

License

The Apache License 2.0, inherited from the base model
ricursiveAI/Ripple-65B-Preview.

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