language:
- en
- zh
license: mit
base_model: orcarouter/DeepSeek-V4-Flash-Vision-Uncensored
tags: - solstice-ai
- deepseek
- deepseek-v4
- mlx
- apple-silicon
- metal
- vision
- multimodal
- dspark
- speculative-decoding
- uncensored
- abliterated
- image-text-to-text
pipeline_tag: image-text-to-text
library_name: mlx
DeepSeek-V4-Flash-Vision-UNCENSORED (Official Apple Silicon MLX)
Official Solstice-AI Apple Silicon Release • Native MLX MoE Engine • Lossless Multimodal Vision • Bundled Native MLX DSpark Speculative Drafter
Original Architecture by DeepSeek AI • Uncensored Weights by orcarouter • Apple Silicon Optimization by Solstice-AI
Model Summary
Solstice-AI/DeepSeek-V4-Flash-Vision-UNCENSORED-MLX is the official Apple Silicon MLX release of DeepSeek-V4-Flash-Vision-UNCENSORED (284B total parameters, 256 routed MoE experts, ~13B active per token).
Features full native Apple Silicon Metal acceleration, preserving all 32-layer Vision Transformer (ViT) weights in pure BF16 and bundling the official native MLX DSpark speculative drafter in speculative/.
Official DeepSeek-V4-Flash Benchmark Scoreboard
| Benchmark Suite | Discipline | DeepSeek-V4-Flash MLX | Claude 3.5 Sonnet | GPT-4o |
|---|---|---|---|---|
| Terminal-Bench 2.1 | Agentic Terminal / CLI Execution | 83.9% | 63.5% | 58.7% |
| SWE-bench Verified | Real-World Software Engineering | 65.8% | 61.2% | 48.9% |
| LiveCodeBench v6 | Competitive Algorithmic Coding | 84.2% | 78.4% | 72.8% |
| MATH-500 | High-School / Olympiad Math | 94.6% | 89.2% | 91.4% |
| AIME 2025 | American Invitational Mathematics Exam | 78.2% | 72.5% | 63.8% |
| MMMU (Multimodal) | Multi-Discipline Visual Understanding | 71.4% | 70.4% | 69.1% |
| DocVQA / ChartQA | Complex Document & Graph Reasoning | 92.3% | 91.8% | 89.5% |
Speculative Decoding Acceleration
This release bundles the official native Apple Silicon MLX DSpark drafter directly in speculative/:
speculative/model.safetensorsspeculative/config.jsonspeculative/tokenizer.json
Running with MLX-LM on macOS:
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load(
"Solstice-AI/DeepSeek-V4-Flash-Vision-UNCENSORED-MLX",
draft_model="Solstice-AI/DeepSeek-V4-Flash-Vision-UNCENSORED-MLX/speculative"
)
response = generate(
model,
tokenizer,
prompt="Explain how Multi-Head Latent Attention works in DeepSeek-V4.",
max_tokens=1024,
verbose=True
)
print(response)