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jeweled/unfiltered-5b-grpo-math-expert-mlx-8Bit

jeweled 5B second-order
Abliteration classifier · v1.0.0
?
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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Downloads · 30-day
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Model age
3d ago
created 2026-09-12
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Metadata

License
other
Languages
en
Tags
transformers safetensors qwen3 text-generation littlelearner unbounded instruct reinforcement-learning mlx mlx-my-repo conversational en

Related

Total size
4.99 GB
Files
9
Quantizations
1
Registered
2026-09-12 13:55
Last updated on HF
2026-09-12 13:39

Files by quantization

Auxiliary files 9 files 4.99 GB
model.safetensors 4.99 GB a9a8de0c download
tokenizer.json 4.41 MB 067f19db download
model.safetensors.index.json 76.3 KB 8bb16e3c download
config.json 2.24 KB 87518984 download
.gitattributes 1.48 KB a6344aac download
README.md 1.12 KB 06b12094 download
tokenizer_config.json 315 B 3aa08d9b download
generation_config.json 235 B 40455263 download
chat_template.jinja 208 B 7d325450 download

README current version from Hugging Face


license: other
language:

  • en
    library_name: transformers
    pipeline_tag: text-generation
    tags:
  • qwen3
  • text-generation
  • littlelearner
  • unbounded
  • instruct
  • reinforcement-learning
  • mlx
  • mlx-my-repo
    base_model: littlelearner/unfiltered-5b-grpo-math-expert

jeweled/unfiltered-5b-grpo-math-expert-mlx-8Bit

The Model jeweled/unfiltered-5b-grpo-math-expert-mlx-8Bit was converted to MLX format from littlelearner/unfiltered-5b-grpo-math-expert using mlx-lm version 0.31.2.

Use with mlx

pip install mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("jeweled/unfiltered-5b-grpo-math-expert-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)

README history 1 version

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

  1. 2026-09-12Upload folder using huggingface_hub90e1cea1.1 KB
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