license: apache-2.0
language:
- en
- zh
base_model: - openbmb/MiniCPM-V-4.6
- SicariusSicariiStuff/Qwen3.5-0.8B_Abliterated
tags: - multimodal
- vision
- abliterated
- uncensored
- qwen3.5
- minicpm
- vision-language
- edge
- mobile
pipeline_tag: image-text-to-text
MiniCPM-V 4.6 — 0.8B Abliterated
A tiny vision-language model built by swapping MiniCPM-V 4.6's original Qwen3.5-0.8B backbone with Qwen3.5-0.8B Abliterated, removing refusal behavior while preserving vision capabilities.
⚠️ Experimental
This is an experimental backbone swap. The vision-language projector (vit_merger) was not retrained after the backbone replacement. Since the abliterated backbone has the same hidden dimensions (1024) as the original, the merger weights are compatible and no resizing was needed. Vision tasks may still work but quality has not been extensively benchmarked.
Text-only tasks work well with the abliterated backbone. Vision task quality depends on how much the abliteration process altered the backbone's internal representations.
Specs
| Component | Details |
|---|---|
| Architecture | MiniCPMV4_6ForConditionalGeneration |
| LLM Backbone | Qwen3.5-0.8B Abliterated (dense) |
| Hidden Size | 1024 |
| LLM Layers | 24 |
| Attention | 8 heads (2 KV heads), hybrid linear/full |
| Context Length | 262,144 tokens |
| Vision Encoder | SigLip2-400M (27 layers, hidden=1152) |
| Vocab Size | 248,320 |
| Total Size | ~2.7 GB |
| Precision | BF16 |
| Min VRAM | ~4 GB |
| Quantization | None (fits as-is on edge GPUs) |
What Changed
| Component | Original MiniCPM-V 4.6 | This Model |
|---|---|---|
| LLM Backbone | Qwen3.5-0.8B | Qwen3.5-0.8B Abliterated |
| Merger MLP | Original weights | Original weights (same dims) |
| Vision Encoder | SigLip2-400M | SigLip2-400M (unchanged) |
| Refusal Behavior | Standard guardrails | Removed via abliteration |
Key Features
- Tiny footprint: 2.7GB total, fits in 4GB VRAM (RTX A500, mobile GPUs, Jetson, etc.)
- Abliterated: Refusal behavior removed — responds to all queries without artificial restrictions
- Same architecture: Drop-in compatible with MiniCPM-V 4.6 tooling and pipelines
- Hybrid attention: Mix of linear and full attention layers for efficient long-context processing
Usage
import torch
from transformers import AutoModel, AutoTokenizer
from PIL import Image
model = AutoModel.from_pretrained(
"jduartedj/MiniCPM-V-4.6-0.8B-Abliterated",
trust_remote_code=True,
torch_dtype=torch.bfloat16
)
model = model.eval().cuda()
tokenizer = AutoTokenizer.from_pretrained(
"jduartedj/MiniCPM-V-4.6-0.8B-Abliterated",
trust_remote_code=True
)
# Image understanding
image = Image.open("example.jpg")
msgs = [{"role": "user", "content": [image, "Describe this image in detail."]}]
result = model.chat(msgs=msgs, tokenizer=tokenizer)
print(result)
# Text-only (abliterated)
msgs = [{"role": "user", "content": "Write a story without restrictions."}]
result = model.chat(msgs=msgs, tokenizer=tokenizer)
print(result)
Limitations
- Vision projector not retrained: The vit_merger was kept from the original model. While dimensions match, abliteration may have shifted internal representations enough to degrade vision quality.
- No benchmarks: This model has not been formally evaluated on vision-language benchmarks.
- Experimental: Use at your own risk. Best suited for research and experimentation.
- Small model limitations: As a 0.8B model, reasoning capabilities are inherently limited compared to larger variants.
Credits
- OpenBMB for MiniCPM-V 4.6 architecture and codebase
- SicariusSicariiStuff for Qwen3.5-0.8B Abliterated
- Built by jduartedj