README.md

---
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, trained on our [BrandSafe-16k](https://huggingface.co/datasets/OverseerAI/BrandSafe-16k) dataset. 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

```python
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}}
}
```