Abliterated Models Download: Safe Sources and Checks

Where people usually download from

Most abliterated weights are published on Hugging Face, the main public hub for open-weight models, either as full-precision checkpoints or as quantized GGUF files for llama.cpp, Ollama and LM Studio. Some uploaders also share files on torrents or file hosts. Treat those with more caution, because you lose the model page, the version history and the community comments that help you judge a release.

A repository with a long history, a clear model card, and discussion threads where other people report how it behaves is a better sign than a new account with one upload and no description. You can read more about the technique in our explainer on what an abliterated model is.

File format decides how much risk you take

The format matters more than most people expect.

• safetensors. Stores plain tensors and cannot run code when loaded. This is the safer choice for full-precision weights.
• GGUF. A format for quantized weights used by llama.cpp and tools built on it. It is also data, not executable code, and it is the usual choice for local chat.
• Pickle-based files (.bin, .pt, .ckpt). Loading these with common Python tools can execute arbitrary code embedded in the file. Avoid them from unknown sources, or load them only in a sandbox.

If a repository offers only pickle files and you cannot find a safetensors or GGUF version, that is a reason to look elsewhere. Also be careful with any repository that ships a custom Python loader script along with the weights. Read that script before you run it, or do not run it.

A checklist before you download

1. Read the model card. It should name the base model, the method used to remove refusals, and the quantization level. A card that says nothing is a warning sign.
2. Check the license. The abliterated copy inherits the terms of the base model. Some licenses restrict commercial use or require attribution. If you plan to build a product, read them first.
3. Compare the base model name with the official release. A copycat uploader may attach a popular name to a different file.
4. Look at the file list and sizes. They should match the parameter count and quantization. A 7B Q4 file that is 400 MB, or a 70B file that is 4 GB, is wrong.
5. Look for a published checksum and verify it after download.
6. Skim the community tab for reports of broken output, repetition or hidden behavior.

Work out the download size first

Approximate file size is parameters x bits per weight / 8. These are weights only, and real files run slightly larger.

Parameters	16-bit	8-bit	4-bit
7B	14 GB	7 GB	3.5 GB
13B	26 GB	13 GB	6.5 GB
34B	68 GB	34 GB	17 GB
70B	140 GB	70 GB	35 GB

You also need free disk for the download and, if you convert or quantize yourself, for the intermediate files. Running out of space halfway through a 70 GB transfer is a common annoyance. Memory to run the file is a separate question: the weights have to fit in GPU or unified memory, with extra room for the context cache.

Verify the file after it arrives

Download into its own empty folder, keep your scripts somewhere else, and check the hash before you load the file.

Copyhuggingface-cli download some-user/some-repo-GGUF file-Q4_K_M.gguf --local-dir ./incoming
cd incoming
shasum -a 256 file-Q4_K_M.gguf
# compare the output with the checksum shown on the repository page

If the uploader does not publish a checksum, the hash only proves the file did not get corrupted in transit, not that it is what the author intended. For anything you plan to depend on, prefer uploaders whose files are also mirrored or discussed by other people. Load new files first on a machine without access to sensitive data.

Keep a note of what you downloaded: the repository name, the date, the file name and the hash. When a better edit appears a month later, you will want to compare the two on the same prompts, and you will want to know exactly which file you tested first.

Finally, test behavior. Run ten to twenty prompts from your actual work and read the outputs. A file can be intact and still be a poor edit: incoherent replies, constant repetition, or refusals that were not fully removed.

Red flags that should stop you

Some signals are enough to walk away without further analysis.

• The repository asks you to disable security scanning, run an installer, or paste a command into a terminal as root.
• The download link points to a file host that is not the repository page, or the page tells you to disable your antivirus.
• The model card promises "100% uncensored, zero loss" or any other claim that cannot be true of an edit to weights. Every edit has a trade-off, and honest cards say so.
• Reuploads of a known release under a new name, with a different file size from the original.
• No license text at all, for a base model that is known to have one.

Even with clean signals, run new files with the least access you can give them. A GGUF opened by a current version of a mainstream runtime carries little risk of code execution, but runtimes have had bugs, so keep them updated. If something looks wrong, delete the file and pick another source. Re-downloading costs minutes; recovering from a bad file on a machine holding your work does not.

When a hosted endpoint saves the time

Downloading makes sense when you want offline use, full control over quantization, or you plan to merge and experiment. If you only want a model that answers without content refusals for lawful adult use, a hosted API saves the download, the storage, the verification and the memory planning.

This service serves one open-weight model, run by an independent provider, under the ID uncensored. It is not a mirror of any vendor's official API. You call it like OpenAI:

Copyfrom openai import OpenAI

client = OpenAI(base_url="https://api.abliteratedmodels.cc/v1", api_key="YOUR_KEY")
r = client.chat.completions.create(
    model="uncensored",
    messages=[{"role": "user", "content": "List five checks for a downloaded file."}],
)
print(r.choices[0].message.content)

The context window is 100,000 tokens. Streaming, tool calling and JSON mode are supported, and prompts are not used for training. Parameters and limits are on the documentation page.

What the API costs against a download

There is no download cost, but you pay per token: $0.15 per 1M input and $0.50 per 1M output. A request with 4,000 input tokens and 800 output tokens costs 4,000 / 1,000,000 x $0.15 = $0.0006 plus 800 / 1,000,000 x $0.50 = $0.0004, so $0.001. A thousand of those cost $1.

The minimum top-up is $20, with +5% from $50, +10% from $100 and +15% from $200. Pay by crypto. See pricing for details. To test with no payment, claim the $0.50 trial on the API key page after sign-up; it is valid for 7 days. For coding use specifically, the page on abliterated models for coding compares what to look for.

More guides

• How to Run Abliterated Models: Local, Cloud or API
• Best Abliterated Models for Coding: What to Look For