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mlx-community/MedraN-E4B-Uncensored-Q4

mlx-community 6.9B second-order
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Response includes
  • classification m-uncensored
  • files 14
  • hub_downloads_all_time 973
  • author_summary 207 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
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Downloads · lifetime
973
77 last 30d - cooling
Likes
0
Model age
7mo ago
created 2026-02-15
Downloads over time
Now1K→from209↑378%
1694737761.1K209 on Feb 181K on Oct 11FebAprJunAugOct
Feb 18 → Oct 11 · 73 snapshots · spans 235 days

Genealogy 0 direct forks

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Metadata

License
other
Tags
mlx safetensors gemma3n medical uncensored quantized base_model:nicoboss/MedraN-E4B-Uncensored-EP7 base_model:finetune:nicoboss/MedraN-E4B-Uncensored-EP7 license:other region:us

Related

Total size
3.60 GB
Files
14
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2026-02-15 22:38

Files by quantization

Auxiliary files 14 files 3.64 GB
model.safetensors 3.60 GB 78c3ca1f download
tokenizer.json 31.9 MB b6c35ee6 download
tokenizer.model 4.48 MB ea5f0cc4 download
tokenizer_config.json 1.15 MB 2861b4a4 download
model.safetensors.index.json 147 KB 10d27819 download
config.json 58.7 KB 13aa238b download
README.md 2.65 KB eee02757 download
.gitattributes 1.64 KB 25d729fa download
chat_template.jinja 1.59 KB a0405ea9 download
preprocessor_config.json 1.07 KB 7bdc98cc download
special_tokens_map.json 769 B 6bb15953 download
generation_config.json 210 B 903dab45 download
README-q4.md 192 B fb8349a8 download
processor_config.json 98.0 B 2ffcf33a download

README current version from Hugging Face


license: other
license_name: custom-license
license_link: https://huggingface.co/nicoboss/MedraN-E4B-Uncensored-EP7/blob/main/LICENSE.txt
base_model: nicoboss/MedraN-E4B-Uncensored-EP7
tags:

  • mlx
  • medical
  • uncensored
  • quantized

Medra3n-E4B-Uncensored-MLX-Quantized

This repository contains quantized MLX-optimized versions of nicoboss/MedraN-E4B-Uncensored-EP7, converted for use with Apple Silicon devices using the MLX framework.

Model Description

MedraN (Medical Reasoning and Analysis) is a specialized language model fine-tuned for medical applications. This E4B (Episode 4B) variant is an uncensored version that provides comprehensive medical information without content restrictions.

Available Quantizations

This repository includes two quantized versions optimized for different use cases:

Q6 Version (6-bit quantization)

  • Size: ~5.2GB
  • Quality: High quality with minimal degradation
  • Use case: Best balance between size and performance
  • Actual quantization: 6.501 bits per weight

Q4 Version (4-bit quantization)

  • Size: ~3.6GB
  • Quality: Good quality with some degradation
  • Use case: Maximum speed and memory efficiency
  • Actual quantization: 4.501 bits per weight

Usage

These models are optimized for use with the MLX framework on Apple Silicon devices. You can use them with:

Q6 Version:

from mlx_lm import load, generate

model, tokenizer = load("drwlf/MedraN-E4B-Uncensored-MLX-Quantized", model_path="q6")
response = generate(model, tokenizer, "What are the symptoms of...", max_tokens=512)

Q4 Version:

from mlx_lm import load, generate

model, tokenizer = load("drwlf/MedraN-E4B-Uncensored-MLX-Quantized", model_path="q4")
response = generate(model, tokenizer, "What are the symptoms of...", max_tokens=512)

Model Comparison

Version Size Quality Speed Memory Usage
Q6 5.2GB High Good Medium
Q4 3.6GB Good Fast Low
Full 13GB Best Slow High

Original Model

This is a conversion of the original model available at: https://huggingface.co/nicoboss/MedraN-E4B-Uncensored-EP7

Full precision MLX version: https://huggingface.co/drwlf/MedraN-E4B-Uncensored-MLX

Conversion Details

  • Framework: MLX
  • Base precision: float16
  • Quantization: 4-bit and 6-bit
  • Optimized for: Apple Silicon (M1/M2/M3/M4 chips)

License

This model follows the same licensing terms as the original model. Please refer to the original model's license for usage terms.

README history 1 version

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

  1. 2026-02-15Duplicate from drwlf/MedraN-E4B-Uncensored-MLX-Quantized3178e752.6 KB
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