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solidrust/Aura_Uncensored_l3_8B-AWQ

solidrust Llama 7.0B second-order
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Response includes
  • classification m-uncensored
  • files 11
  • hub_downloads_all_time 302
  • author_summary 16 models
  • readme_text full
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Abliteration classifier · v1.0.0
M-U
Primary method

Uncensored (method unknown)

No other method signals detected in this model.
Confidence
LOW
Why this label 3 signals
Weak or ambiguous signals. Best guess based on catalog patterns; treat as tentative and check the evidence below.
  • 'uncensored' in name/tags but no 'abliterated' marker
  • method not identifiable from author declaration alone
  • may be DPO fine-tune, prompt engineering, or unknown technique
Refusal direction extraction

No specific extraction method could be identified for this model. The producer either did not document it or used a proprietary pipeline.

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Downloads · lifetime
302
13 last 30d - cooling
Likes
0
Model age
2.5y ago
created 2024-04-23
Downloads over time
Now309→from8↑3,763%
01132263408 on Jul 24, 2024309 on Oct 11309 on Oct 10Jul '24Nov '24Mar '25Jul '25Nov '25MarJul
Jul 24, 2024 → Oct 11 · 155 snapshots · spans 809 days

Genealogy 0 direct forks

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Metadata

Tags
transformers safetensors llama text-generation 4-bit AWQ autotrain_compatible endpoints_compatible conversational base_model:ResplendentAI/Aura_Uncensored_l3_8B base_model:quantized:ResplendentAI/Aura_Uncensored_l3_8B text-generation-inference

Related

Total size
5.33 GB
Files
11
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2024-09-03 08:10

Files by quantization

Auxiliary files 11 files 5.34 GB
model-00001-of-00002.safetensors 4.36 GB b1c2b44d download
model-00002-of-00002.safetensors 1002 MB 289211f0 download
tokenizer.json 8.66 MB 5c903c01 download
model.safetensors.index.json 62.0 KB 1685b84f download
tokenizer_config.json 49.8 KB 0c4c5015 download
README.md 2.80 KB efdbf568 download
.gitattributes 1.48 KB a6344aac download
config.json 982 B 158f3abb download
special_tokens_map.json 449 B e5b39b63 download
generation_config.json 164 B d5ccca86 download
quant_config.json 82.0 B 4002036f download

README current version from Hugging Face


base_model: ResplendentAI/Aura_Uncensored_l3_8B
library_name: transformers
tags:

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

ResplendentAI/Aura_Uncensored_l3_8B 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/Aura_Uncensored_l3_8B-AWQ"
system_message = "You are Aura_Uncensored_l3_8B, incarnated as a powerful AI. You were created by ResplendentAI."

# 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 3 versions

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

  1. 2024-09-03Added base_model tag in README.md15e113e2.8 KB
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  2. 2024-04-23adding initial model card96b22b02.8 KB
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  3. 2024-04-23add processing notice83351481.2 KB
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