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

solidrust Gemma 8.3B second-order
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Response includes
  • classification m1
  • files 11
  • benchmarks 5 entries
  • hub_downloads_all_time 260
  • author_summary 16 models
  • readme_text full
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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
260
21 last 30d - cooling
Likes
0
Model age
2.1y ago
created 2024-09-20
Downloads over time
Now271→from1↑27,000%
01282573851 on Sep 18, 2024271 on Oct 11350 on Sep 10, 2025Sep '24Jan '25May '25Sep '25JanMaySep
Sep 18, 2024 → Oct 11 · 147 snapshots · spans 753 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

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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
2024-09-20 13:50

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 2 versions

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

  1. 2024-09-20Update README after successful quantization0717cc32.8 KB
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  2. 2024-09-20Add processing noticef5bfd0c1.2 KB
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