Text Classification
Transformers
Safetensors
llama
text-generation
brand-safety
content-moderation
apple-silicon
metal
mps
Eval Results (legacy)
text-embeddings-inference
Instructions to use UnionStreet/vision-1-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UnionStreet/vision-1-mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="UnionStreet/vision-1-mini")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("UnionStreet/vision-1-mini") model = AutoModelForCausalLM.from_pretrained("UnionStreet/vision-1-mini") - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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---
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language:
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- en
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- de
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- fr
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- it
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license: llama3.1
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library_name: transformers
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pipeline_tag: text-classification
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tags:
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- facebook
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- meta
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- pytorch
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- llama
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- brand-safety
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- classification
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model-index:
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- name: vision-1-mini
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results:
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- task:
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type: text-classification
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name: Brand Safety Classification
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metrics:
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- type: accuracy
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value: 0.95
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name: Classification Accuracy
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datasets:
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- BrandSafe-16k
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metrics:
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- accuracy
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base_model: meta-llama/Llama-2-8b-chat
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model_size: "4.58 GiB"
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parameters: "8.03B"
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quantization: "GGUF V3"
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architectures:
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- LlamaForCausalLM
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model_parameters:
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block_count: 32
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context_length: 131072
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embedding_length: 4096
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feed_forward_length: 14336
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attention_heads: 32
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kv_heads: 8
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rope_freq_base: 500000
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vocab_size: 128256
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hardware:
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recommended: "Apple Silicon"
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memory:
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cpu_kv_cache: "992.00 MiB"
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metal_kv_cache: "32.00 MiB"
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metal_compute: "560.00 MiB"
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cpu_compute: "560.01 MiB"
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inference:
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load_time: "3.27s"
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device: "Metal (Apple M3 Pro)"
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memory_footprint:
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cpu: "4552.80 MiB"
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metal: "132.50 MiB"
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---
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# vision-1-mini
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Vision-1-mini is an optimized 8B parameter model based on Llama 3.1, specifically designed for brand safety classification
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## Model Details
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# vision-1-mini
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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.
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## Model Details
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