DeathGodlike/DarkArtsForge_Avnas-7B-v1_EXL3

Source model

Avnas-7B-v1 by DarkArtsForge

---

Provided quantized models

ExLlamaV3: release v0.0.20

Type	Size	CLI
H8-4.0BPW	3.89 GB	Copy-paste the line / Download the batch file
H8-6.0BPW	5.64 GB	Copy-paste the line / Download the batch file
H8-8.0BPW	7.38 GB	Copy-paste the line / Download the batch file

Requirements: A python installation with huggingface-hub module to use CLI.

Licensing

License detected: apache-2.0

The license for the provided quantized models is inherited from the source model (which incorporates the license of its original base model). For definitive licensing information, please refer first to the page of the source or base models. File and page backups of the source model are provided below.

---

Backups

Date: 23.01.2026

Source files

Source page (click to expand)

⚠️ Warning: This model can produce narratives and RP that contain violent and graphic erotic content. Adjust your system prompt accordingly, and use Alpaca template.

Avnas 7B v1

This is my first model finetune so it may be a bit rough around the edges.

mistralai/Mistral-7B-v0.1 was lightly trained on a custom dataset to uncensor the model and train it on Cthulhu mythos.

v1.0 Training Steps: 100

The model appears to have no refusals and is fully uncensored, with no ablation needed.

A version 2 might be released with larger dataset and longer cook time. This was mainly just proof of concept and it seems to work well.

Avnas was created using a custom finetuner kit I made called PMPF (Poor Man's Portable Finetuner). This allows finetuning with only 4-12GB VRAM. I could not get Axolotl, Unsloth, or any other tools working locally on Windows, so I made my own.

Note: Use Alpaca template to prevent errors. The dataset was specifically calibrated using Alpaca format due to issues with ChatML tokenizer.

\n### Instruction:\n \n### Response:\n

Update: The finetune EOS padding bug and safetensors were patched.

<<<<<<
    # --- 4. Load Tokenizer ---
    tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, local_files_only=True)
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token
    tokenizer.padding_side = "right"
======
    # --- 4. Load Tokenizer ---
    tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, local_files_only=True)
    
    # FIX: Use <unk> (ID 0) for padding instead of EOS (ID 2)
    # This prevents the model from learning to stop generating prematurely
    tokenizer.pad_token_id = 0  # unk_token_id for Mistral/Llama
    tokenizer.padding_side = "right"
>>>>>>