Update README.md

README.md

@@ -1,3 +1,180 @@
----
-license: llama3.1
----
+---
+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
+
+```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}}
+}
+```