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Jhustle44/gemma-3-1b-it-abliterated-mlx-8Bit

Jhustle44 Gemma 1B second-order
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     "https://abliteration.org/api/v1/models/Jhustle44%2Fgemma-3-1b-it-abliterated-mlx-8Bit"
Response includes
  • classification unknown
  • files 10
  • author_summary 1 models
  • readme_text full
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Abliteration classifier · v1.0.0
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Primary method

Unclassified

No clear signals of an abliteration technique in this model.
Confidence
UNKNOWN
Why this label 1 signal
No classification signals present. This may not be an abliterated model at all - it could be a repackaging, a merge with unrelated goals, or unrelated content that mentions the term.
  • no classification signals present (no abliterated, uncensored, or known producer/method markers)
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Model age
today
created 2026-10-11

Genealogy 0 direct forks

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Metadata

Tags
transformers safetensors gemma3_text text-generation mlx mlx-my-repo conversational base_model:mlabonne/gemma-3-1b-it-abliterated base_model:quantized:mlabonne/gemma-3-1b-it-abliterated text-generation-inference endpoints_compatible 8-bit

Related

Total size
1.29 GB
Files
10
Quantizations
1
Registered
2026-10-11 00:59
Last updated on HF
2026-10-11 00:11

Files by quantization

Auxiliary files 10 files 1.33 GB
model.safetensors 1.29 GB 137863dd download
tokenizer.json 31.8 MB 4667f208 download
tokenizer.model 4.47 MB 1299c11d download
tokenizer_config.json 1.10 MB 7bdd14f0 download
model.safetensors.index.json 49.4 KB da22cc86 download
.gitattributes 1.53 KB 52373fe2 download
config.json 1.10 KB 2168cead download
README.md 963 B aae95977 download
special_tokens_map.json 662 B 1a619324 download
added_tokens.json 35.0 B e17bde03 download

README current version from Hugging Face


library_name: transformers
tags:

  • mlx
  • mlx-my-repo
    base_model: mlabonne/gemma-3-1b-it-abliterated

erichartford/gemma-3-1b-it-abliterated-mlx-8Bit

The Model erichartford/gemma-3-1b-it-abliterated-mlx-8Bit was converted to MLX format from mlabonne/gemma-3-1b-it-abliterated using mlx-lm version 0.22.1.

Use with mlx

pip install mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("erichartford/gemma-3-1b-it-abliterated-mlx-8Bit")

prompt="hello"

if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)
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