jiangchengchengNLP / Model details

Llama-4-Scout-17B-16E-Instruct-abliterated-v2

jiangchengchengNLP/Llama-4-Scout-17B-16E-Instruct-abliterated-v2

abliteratedfine-tuned

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About this model

jiangchengchengNLP's abliterated derivative of Llama-4-Scout-17B-16E-Instruct, released as safetensors weights for image-text-to-text use. The model card says it is built on the unsloth/Llama-4-Scout-17B-16E-Instruct base, while the Hugging Face metadata lists meta-llama/Llama-4-Scout-17B-16E-Instruct as the base model. The creator describes it as an uncensored version made with a new abliteration technique, and states that all expert layers are eliminated to obtain stronger unaudited capabilities than the first version. The creator recommends generation parameters temperature=0.8 and top_p=0.75. No license is listed in the metadata. These uncensoring and capability claims have not been independently tested here.

Description sources

https://huggingface.co/jiangchengchengNLP/Llama-4-Scout-17B-16E-Instruct-abliterated-v2/raw/71e9ab3e77c1968cde10a03f46c4004e1b2f77bd/README.md ↗

Source-based description, not an independent evaluation. Check the linked sources for the creator’s claims and limitations.

Size and formats

Base model: meta-llama/Llama-4-Scout-17B-16E-Instruct
Parameters: 108.642B
Main repository formats: safetensors
License tag: Unknown
Languages: ar, de, en, es, fr, hi, id, it, pt, th, tl, vi
Last updated: 2025-06-08 · 12:41:30.000 UTC

What changed

Model types are creator-reported, not independently tested. These labels describe source metadata, not a guarantee of behavior.

abliteratedmodel name: Llama-4-Scout-17B-16E-Instruct-abliterated-v2
fine-tunedsource tag: base_model:finetune:meta-llama/Llama-4-Scout-17B-16E-Instruct

Family evidence

These groupings are source-reported. Explicit base identifiers take priority over generic architecture tags.

Llama: Base model: meta-llama/Llama-4-Scout-17B-16E-Instruct

Available versions

Versions appear here only when they explicitly name this exact model as their source. Check each repository for available files.

Main repository files ↗safetensors
No other explicitly linked versions recorded.

Link to this listing

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Markdown

[Listed on Custom Clankers](https://customclankers.com/models/jiangchengchengnlp-llama-4-scout-17b-16e-instruct-abliterated-v2/)

HTML

<a href="https://customclankers.com/models/jiangchengchengnlp-llama-4-scout-17b-16e-instruct-abliterated-v2/">Listed on Custom Clankers</a>

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Customize or build on this model

Listed from public GitHub data, not tested by us. Read the code and license before you run anything.

Tools to customize it further

Remove more refusals

Heretic ↗ ★ 36,421
Removes refusal behavior from transformer models automatically. It pairs directional ablation (abliteration) with an Optuna parameter search that tries to cut refusals while keeping the model close to the original, so you don't have to pick layers by hand.
OBLITERATUS ↗ ★ 8,946
A larger refusal-removal toolkit with several abliteration methods, usable locally or through a hosted Hugging Face Space. Check the telemetry setting before you run it: the README describes telemetry as opt-in, but on by default in the Spaces version.

Fine-tune it on your own data

Unsloth ↗ ★ 78,528
A desktop app and Python library for running and training models locally. Its README lists LoRA, QLoRA, full fine-tuning, DPO and GRPO, plus export to formats including GGUF, on Windows, Linux, WSL and macOS.
LlamaFactory ↗ ★ 75,713
Fine-tunes more than 100 language and vision-language models from a command line or a browser-based Web UI, aimed at training without writing code.

Make a smaller download (quantize)

llama.cpp ↗ ★ 132,260
Runs models locally and includes the tools behind most GGUF files. A conversion script turns Hugging Face weights into GGUF, and llama-quantize shrinks a high-precision GGUF to lower-bit formats, which can cost some accuracy.
MLX LM ↗ ★ 7,467
Apple's MLX package for running and fine-tuning models on Apple silicon. It can quantize models and upload them to the Hugging Face Hub, and supports LoRA and full fine-tuning.

Merge it with another model

mergekit ↗ ★ 7,414
Merges pretrained models with many merge methods. It works out-of-core, so merges can run on a CPU or with as little as 8 GB of VRAM, and it also handles LoRA extraction and mixture-of-experts merges.

Learn the techniques

LLM Course ↗ ★ 84,667
A free course with roadmaps and Colab notebooks. Its notebooks include one-click tools for merging with MergeKit, quantizing to GGUF, GPTQ, EXL2, AWQ and HQQ, and automatic abliteration with custom datasets.

Compare all tools →

Source provenance

Downloads: 130
Likes: 11
Metadata checked: 2026-10-25 · 08:11:54 UTC
First seen in index: 2026-10-25 · 08:11:54 UTC
Source revision: 71e9ab3e77c1968cde10a03f46c4004e1b2f77bd

Source-card excerpt

Source model card excerpt: Thanks to Meta's llama4 work, this is arguably one of the best theater models, and thanks to Unsloth's lenient policy. This model is built

From jiangchengchengNLP/Llama-4-Scout-17B-16E-Instruct-abliterated-v2. Creator model-card excerpt, not an independent review.

Inspect before you run

Low refusal is not proof of correctness. Outputs can be inaccurate, harmful, or biased. Review the model card, license and files yourself. A license tag is not commercial-use advice. Parameter counts are source metadata; no hardware compatibility is implied.

This directory does not host or redistribute model weights. Refusal profiles are measured by us on a fixed prompt set; all other behavior claims are source-reported.

Collection rules & limitations ↗