tinyopsec/Huihui-MiniCPM5-2B-abliterated-GGUF overview
Huihui MiniCPM5 2B abliterated — GGUF Quantizations This repository contains GGUF quantizations of huihui ai/Huihui MiniCPM5 2B abliterated https://huggingface…
Runs locally from ~991.8 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| model_f16.gguf | GGUF | F16 | 4.69 GB | Download |
| model_q2_k.gguf | GGUF | Q2_K | 991.8 MB | Download |
| model_q3_k_l.gguf | GGUF | Q3_K_L | 1.28 GB | Download |
| model_q3_k_m.gguf | GGUF | Q3_K_M | 1.20 GB | Download |
| model_q3_k_s.gguf | GGUF | Q3_K_S | 1.11 GB | Download |
| model_q4_k_m.gguf | GGUF | Q4_K_M | 1.45 GB | Download |
| model_q4_k_s.gguf | GGUF | Q4_K_S | 1.40 GB | Download |
| model_q5_k_m.gguf | GGUF | Q5_K_M | 1.68 GB | Download |
| model_q5_k_s.gguf | GGUF | Q5_K_S | 1.65 GB | Download |
| model_q6_k.gguf | GGUF | Q6_K | 1.93 GB | Download |
| model_q8_0.gguf | GGUF | Q8_0 | 2.50 GB | Download |
Model Details
| Model ID | tinyopsec/Huihui-MiniCPM5-2B-abliterated-GGUF |
|---|---|
| Author | tinyopsec |
| Pipeline | text-generation |
| License | apache-2.0 |
| Base model | huihui-ai/Huihui-MiniCPM5-2B-abliterated |
| Last modified | 2026-09-13T16:20:57.000Z |
Model README
---
license: apache-2.0
base_model: huihui-ai/Huihui-MiniCPM5-2B-abliterated
tags:
- llama-cpp
- gguf
- minicpm
- quantized
- abliterated
language:
- en
pipeline_tag: text-generation
library_name: gguf
---
Huihui-MiniCPM5-2B-abliterated — GGUF Quantizations
This repository contains GGUF quantizations of huihui-ai/Huihui-MiniCPM5-2B-abliterated.
---
📦 Available Quantizations
| File | Bits | Size (approx.) | Use case |
|------|------|----------------|----------|
| model_f16.gguf | 16-bit | ~5.0 GB | Maximum quality, reference |
| model_q8_0.gguf | 8-bit | ~2.7 GB | Near-lossless, high VRAM |
| model_q6_k.gguf | 6-bit | ~2.1 GB | Excellent quality |
| model_q5_k_m.gguf | 5-bit | ~1.8 GB | Great quality/size balance |
| model_q5_k_s.gguf | 5-bit | ~1.7 GB | Slightly smaller than K_M |
| model_q4_k_m.gguf | 4-bit | ~1.5 GB | Recommended default ✅ |
| model_q4_k_s.gguf | 4-bit | ~1.4 GB | Smaller 4-bit variant |
| model_q3_k_l.gguf | 3-bit | ~1.2 GB | Low VRAM, decent quality |
| model_q3_k_m.gguf | 3-bit | ~1.1 GB | Balanced 3-bit |
| model_q3_k_s.gguf | 3-bit | ~1.0 GB | Minimum 3-bit |
| model_q2_k.gguf | 2-bit | ~0.8 GB | Extreme compression |
---
🚀 Usage
llama.cpp
./llama-cli -m model_q4_k_m.gguf -p "Your prompt here" -n 512
LM Studio
Download any .gguf file and load it directly in LM Studio.
Ollama
ollama run hf.co/tinyopsec/Huihui-MiniCPM5-2B-abliterated-GGUF:Q4_K_M
Python (llama-cpp-python)
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="tinyopsec/Huihui-MiniCPM5-2B-abliterated-GGUF",
filename="model_q4_k_m.gguf",
)
output = llm("Your prompt here", max_tokens=512)
print(output["choices"][0]["text"])
---
🔧 Quantization Details
- Source model: huihui-ai/Huihui-MiniCPM5-2B-abliterated
- Tool: llama.cpp
- Base format: F16 GGUF
---
💡 Which quant should I use?
| VRAM | Recommended |
|------|-------------|
| 1 GB | Q2_K |
| 2 GB | Q3_K_M |
| 3 GB | Q4_K_M ✅ |
| 4 GB | Q5_K_M |
| 6 GB | Q6_K |
| 8 GB+ | Q8_0 / F16 |
---
📄 License
Refer to the original model license.
Run tinyopsec/Huihui-MiniCPM5-2B-abliterated-GGUF with guIDE
Download guIDE — the AI-native code editor with local LLM inference and 69 built-in tools.
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