← back to catalog · registered 2026-08-22 13:56

THRAXAPP/gemma-2-9b-it-abliterated-AWQ

THRAXAPP Gemma 8.3B second-order
Your rig guess connected
? Why do I need an app?
Reading your rig…

This is a rough estimate. Install the free app - we'll show exact numbers.

Reading real hardware from your app right now. Numbers below are exact.

Below is the per-quantization compatibility for this model.

curl -H "Authorization: Bearer $ABL_KEY" \
     "https://abliteration.org/api/v1/models/THRAXAPP%2Fgemma-2-9b-it-abliterated-AWQ"
Response includes
  • classification m1
  • files 11
  • benchmarks 5 entries
  • hub_downloads_all_time 47
  • author_summary 1 models
  • readme_text full
10 credits · hourly refresh · ~4 KB payload Get an API key →
Abliteration classifier · v1.0.0
M1
Primary method

Direct removal

No other method signals detected in this model.
Confidence
MEDIUM
Why this label 3 signals
Method inferred from partial signals - repository name, related files, or tag patterns. Producer identity not confirmed; label may sharpen or shift as we gather more evidence.
  • 'abliterated' in name/tags
  • is_gguf=0 (base model)
  • no specific method indicators - defaulting to M1 (most common)
Refusal direction extracted via
Extraction technique

Difference-of-means

Confidence
MEDIUM
Why we say so
primary_method=M1; difference-of-means is the reference extraction for M1/M3 (Arditi 2024)
Downloads · lifetime
47
8 last 30d - stable
Likes
0
Model age
6mo ago
created 2026-03-31
Downloads over time
Now51→from5↑920%
32038565 on Apr 151 on Oct 1151 on Oct 8AprMayJunJulAugSepOct
Apr 1 → Oct 11 · 67 snapshots · spans 193 days

Benchmarks

Benchmark Score Source
BBH average 0.5336619535607732 OpenLLM-v2
IFEval instruct 0.7865707434052758 OpenLLM-v2
IFEval-Prompt 0.7079482439926063 OpenLLM-v2
MATH lvl 5 0.0007552870090634441 OpenLLM-v2
MMLU-Pro 0.39153922872340424 OpenLLM-v2

Genealogy 0 direct forks

Full fork graph →

This model's place in the market. Above: what it was derived from. Below: the tree of everything derived from it.

Metadata

Tags
transformers safetensors gemma2 text-generation 4-bit AWQ autotrain_compatible endpoints_compatible conversational base_model:IlyaGusev/gemma-2-9b-it-abliterated base_model:quantized:IlyaGusev/gemma-2-9b-it-abliterated text-generation-inference

Related

Total size
7.45 GB
Files
11
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2026-03-31 18:11

Files by quantization

Auxiliary files 11 files 7.47 GB
model-00001-of-00002.safetensors 6.37 GB 5565d51c download
model-00002-of-00002.safetensors 1.08 GB e62f7434 download
tokenizer.json 16.7 MB 7da53ca2 download
tokenizer.model 4.04 MB 61a7b147 download
model.safetensors.index.json 89.2 KB 36be0ee7 download
tokenizer_config.json 39.5 KB 921acf3e download
README.md 2.80 KB 035206e2 download
.gitattributes 1.53 KB 52373fe2 download
config.json 1.09 KB 5602d3e0 download
special_tokens_map.json 636 B 8d6368f7 download
generation_config.json 267 B 7b40cf0a download

README current version from Hugging Face


base_model: IlyaGusev/gemma-2-9b-it-abliterated
library_name: transformers
tags:

  • 4-bit
  • AWQ
  • text-generation
  • autotrain_compatible
  • endpoints_compatible
    pipeline_tag: text-generation
    inference: false
    quantized_by: Suparious

IlyaGusev/gemma-2-9b-it-abliterated AWQ

How to use

Install the necessary packages

pip install --upgrade autoawq autoawq-kernels

Example Python code

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer

model_path = "solidrust/gemma-2-9b-it-abliterated-AWQ"
system_message = "You are gemma-2-9b-it-abliterated, incarnated as a powerful AI. You were created by IlyaGusev."

# Load model
model = AutoAWQForCausalLM.from_quantized(model_path,
                                          fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(model_path,
                                          trust_remote_code=True)
streamer = TextStreamer(tokenizer,
                        skip_prompt=True,
                        skip_special_tokens=True)

# Convert prompt to tokens
prompt_template = """\
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"""

prompt = "You're standing on the surface of the Earth. "\
        "You walk one mile south, one mile west and one mile north. "\
        "You end up exactly where you started. Where are you?"

tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
                  return_tensors='pt').input_ids.cuda()

# Generate output
generation_output = model.generate(tokens,
                                  streamer=streamer,
                                  max_new_tokens=512)

About AWQ

AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.

AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.

It is supported by:

README history 1 version

The author's README evolved over time. Click a version to see its content at that point.

  1. 2026-03-31Duplicate from solidrust/gemma-2-9b-it-abliterated-AWQ0e549922.8 KB
    Loading...
Catalog is the map. Apps are the tools.

Run models on your own machine, not in the cloud.

Every model page has an "Open in Abliteration" button that hands the model directly to the first-party desktop client, at the quantization your rig can actually run. No API keys, no subscription, no prompt leakage.

Open in Abliteration