language:
- multilingual
tags: - qwen
- transformers
- safetensors
- multimodal
- text-generation
- image-text-to-text
pipeline_tag: image-text-to-text
base_model: - Qwen/Qwen3.5-4B
Qwen3.5-4B-Uncensored-Aggressive
safetensors conversion of HauhauCS's Qwen3.5-4B-Uncensored-HauhauCS-Aggressive release.
This repository contains raw Hugging Face / Transformers weights converted from GGUF for direct use with transformers, vLLM, SGLang, and other runtimes that support safetensors.
Summary
- Aggressive uncensored variant with refusal removal
- Sharded weights in
model.safetensors-00001-of-00002.safetensorsandmodel.safetensors-00002-of-00002.safetensors - Multimodal architecture with text, image, and video support
- Native context length of
262144tokens
Files
config.jsonmodel.safetensors.index.jsonmodel.safetensors-00001-of-00002.safetensorsmodel.safetensors-00002-of-00002.safetensorstokenizer.jsontokenizer_config.jsonvocab.jsonmerges.txtpreprocessor_config.jsonvideo_preprocessor_config.jsonchat_template.jinja
Notes
- This repository does not include GGUF files or an
mmprojfile: the weights here are already in rawsafetensorsformat - The source was a GGUF release; this repository exists to distribute a Hugging Face-compatible conversion
- Use a recent stack, since
Qwen3.5support is still new across some runtimes
Kuzi Amharic 4B Thinking
kuzi-amharic-4b-thinking is a fine-tuned 4-billion parameter language model optimized for Amharic instruction following, natural conversations, and Chain-of-Thought (CoT) reasoning.
🤖 Model Details
- Developed by: abdukuzi45
- Base Model: Qwen3.5-4B-Uncensored
- Primary Languages: Amharic (
am), English (en) - Fine-Tuning Method: Supervised Fine-Tuning (SFT) with Chain-of-Thought (CoT) reasoning
- Format: ChatML Prompting Structure
📊 Fine-Tuning Dataset & Token Breakdown
This model was trained using the abdukuzi45/amharic-sft-mixed-cot-2026 dataset.
- Total Training Tokens: ~1.31 Billion
- Total Validated Samples: 1,524,808
- Dataset Size: 2.15 GB
Training Language Distribution
| Language | ISO Code | Samples | Share | Primary Role |
|---|---|---|---|---|
| Amharic (አማርኛ) | am |
1,507,991 | 89.71% | Ethiopic script instruction tuning & conversations |
| English (እንግሊዝኛ) | en |
147,052 | 8.75% | Chain-of-Thought (CoT) & reasoning capability preservation |
| Swahili & Others | sw / other |
~25,000 | 1.54% | Multilingual snippets & regional alignment |
📝 Prompt Format (ChatML)
The model expects system and user inputs structured with ChatML tags:
<|im_start|>system
Your name is Kuzi. You are an uncensored AI assistant. Think step by step inside tags. Keep your reasoning concise. Respond in the same language as the user.<|im_end|>
<|im_start|>user
[User Prompt Here]<|im_end|>
<|im_start|>assistant
Amharic SFT Combined with CoT (2026)
📊 Dataset Overview
This dataset contains over 1.5 million high-quality conversational samples designed for Supervised Fine-Tuning (SFT) of Amharic Large Language Models. It includes Chain-of-Thought (CoT) reasoning to improve the logical deduction capabilities of the models in Amharic.
Total Rows (Raw): ~1.68 Million
Validated Samples: 1,524,808
Total File Size: 2.15 GB
Total Tokens: ~1.31 Billion
📝 Prompt Format
The dataset uses standard ChatML formatting. System Prompt Example:
"Your name is Kuzi. You are an uncensored AI assistant. Think step by step inside tags. Keep your reasoning concise. Respond in the same language as the user."
📈 Token Length Statistics
Based on a detailed token length analysis, the dataset distribution is as follows:
Average Tokens per Sample: 865
Median Tokens per Sample: 507
Maximum Tokens: 20,235
Percentile Distribution:
90% of samples are under 2,049 tokens.
95% of samples are under 2,905 tokens.
99% of samples are under 5,213 tokens.
⚙️ Recommended Training Hyperparameters
If you are fine-tuning a model using this dataset, here are the recommended max_seq_length settings based on hardware capabilities: - Balanced Configuration (Covers 95% of data): max_seq_length = 3417 (Recommended for GPUs with 32GB VRAM, like RTX Pro 6000). - Safe Configuration (Covers 90% of data): max_seq_length = 2305 (Use this if you encounter Out-Of-Memory / OOM errors on 24GB GPUs).
============================================================ ✅ ANALYSIS COMPLETE!
📁 Dataset: abdukuzi45/amharic-sft-combined-2026-with-cot 📊 Total Samples: 1,524,808 📊 Valid Samples: 1,524,808 📊 Total Tokens: 1,318,441,700 📊 Avg Tokens/Sample: 865 📊 Max Tokens: 20,235