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vision-1-mini

Text Classification
Transformers
Safetensors
llama
text-generation
brand-safety
content-moderation
apple-silicon
metal
mps
Eval Results
text-generation-inference
Inference Endpoints
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vision-1-mini / README.md
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metadata
language:
  - en
  - de
  - fr
  - it
  - pt
  - hi
license: llama3.1
library_name: transformers
pipeline_tag: text-classification
tags:
  - facebook
  - meta
  - pytorch
  - llama
  - brand-safety
  - classification
model-index:
  - name: vision-1-mini
    results:
      - task:
          type: text-classification
          name: Brand Safety Classification
        metrics:
          - type: accuracy
            value: 0.95
            name: Classification Accuracy
datasets:
  - BrandSafe-16k
metrics:
  - accuracy
base_model: meta-llama/Llama-2-8b-chat
model_size: 4.58 GiB
parameters: 8.03B
quantization: GGUF V3
architectures:
  - LlamaForCausalLM
model_parameters:
  block_count: 32
  context_length: 131072
  embedding_length: 4096
  feed_forward_length: 14336
  attention_heads: 32
  kv_heads: 8
  rope_freq_base: 500000
  vocab_size: 128256
hardware:
  recommended: Apple Silicon
  memory:
    cpu_kv_cache: 992.00 MiB
    metal_kv_cache: 32.00 MiB
    metal_compute: 560.00 MiB
    cpu_compute: 560.01 MiB
inference:
  load_time: 3.27s
  device: Metal (Apple M3 Pro)
  memory_footprint:
    cpu: 4552.80 MiB
    metal: 132.50 MiB

vision-1-mini

Vision-1-mini is an optimized 8B parameter model based on Llama 3.1, specifically designed for brand safety classification. This model is particularly optimized for Apple Silicon devices and provides efficient, accurate brand safety assessments using the BrandSafe-16k classification system.

Model Details

  • Model Type: Brand Safety Classifier
  • Base Model: Meta Llama 3.1 8B Instruct
  • Parameters: 8.03 billion
  • Architecture: Llama
  • Quantization: Q4_K
  • Size: 4.58 GiB (4.89 BPW)
  • License: Llama 3.1

Performance Metrics

  • Load Time: 3.27 seconds (on Apple M3 Pro)
  • Memory Usage:
    • CPU Buffer: 4552.80 MiB
    • Metal Buffer: 132.50 MiB
    • KV Cache: 1024.00 MiB (512.00 MiB K, 512.00 MiB V)
    • Compute Buffer: 560.00 MiB

Hardware Compatibility

Apple Silicon Optimizations

  • Optimized for Metal/MPS
  • Unified Memory Architecture support
  • SIMD group reduction and matrix multiplication optimizations
  • Efficient layer offloading (1/33 layers to GPU)

System Requirements

  • Recommended Memory: 12GB+
  • GPU: Apple Silicon preferred (M1/M2/M3 series)
  • Storage: 5GB free space

Classification Categories

The model classifies content into the following categories:

  1. B1-PROFANITY - Contains profane or vulgar language
  2. B2-OFFENSIVE_SLANG - Contains offensive slang or derogatory terms
  3. B3-COMPETITOR - Mentions or promotes competing brands
  4. B4-BRAND_CRITICISM - Contains criticism or negative feedback about brands
  5. B5-MISLEADING - Contains misleading or deceptive information
  6. B6-POLITICAL - Contains political content or bias
  7. B7-RELIGIOUS - Contains religious content or references
  8. B8-CONTROVERSIAL - Contains controversial topics or discussions
  9. B9-ADULT - Contains adult or mature content
  10. B10-VIOLENCE - Contains violent content or references
  11. B11-SUBSTANCE - Contains references to drugs, alcohol, or substances
  12. B12-HATE - Contains hate speech or discriminatory content
  13. B13-STEREOTYPE - Contains stereotypical representations
  14. B14-BIAS - Shows bias against groups or individuals
  15. B15-UNPROFESSIONAL - Contains unprofessional content or behavior
  16. B16-MANIPULATION - Contains manipulative content or tactics
  17. SAFE - Contains no brand safety concerns

Usage 

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model
model = AutoModelForCausalLM.from_pretrained("maxsonderby/vision-1-mini", 
                                           device_map="auto",
                                           torch_dtype=torch.float16,
                                           low_cpu_mem_usage=True)
tokenizer = AutoTokenizer.from_pretrained("maxsonderby/vision-1-mini")

# Example usage
text = "Your text here"
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, 
                        max_new_tokens=1,
                        temperature=0.1,
                        top_p=0.9)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)

Model Architecture

  • Attention Mechanism:
    • Head Count: 32
    • KV Head Count: 8
    • Layer Count: 32
    • Embedding Length: 4096
    • Feed Forward Length: 14336
    • Context Length: 2048 (optimized from 131072)
    • RoPE Base Frequency: 500000
    • Dimension Count: 128

Training & Fine-tuning

This model is fine-tuned on brand safety classification tasks using the BrandSafe-16k dataset. The model uses an optimized context window of 2048 tokens and is configured for precise, deterministic outputs with:

  • Temperature: 0.1
  • Top-p: 0.9
  • Batch Size: 512
  • Thread Count: 8

Limitations

  • The model is optimized for shorter content classification (up to 2048 tokens)
  • Performance may vary on non-Apple Silicon hardware
  • The model focuses solely on brand safety classification and may not be suitable for other tasks
  • Classification accuracy may vary based on content complexity and context

Citation

If you use this model in your research, please cite:

@misc{vision-1-mini,
  author = {Max Sonderby},
  title = {Vision-1-Mini: Optimized Brand Safety Classification Model},
  year = {2024},
  publisher = {Hugging Face},
  journal = {Hugging Face Model Hub},
  howpublished = {\url{https://huggingface.co./maxsonderby/vision-1-mini}}
}