Image-Text-to-Text
Transformers
TensorBoard
Safetensors
vision-encoder-decoder
Generated from Trainer
Instructions to use ChayanM/ViT-Bert_Mimic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ChayanM/ViT-Bert_Mimic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="ChayanM/ViT-Bert_Mimic")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("ChayanM/ViT-Bert_Mimic") model = AutoModelForImageTextToText.from_pretrained("ChayanM/ViT-Bert_Mimic") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ChayanM/ViT-Bert_Mimic with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ChayanM/ViT-Bert_Mimic" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ChayanM/ViT-Bert_Mimic", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ChayanM/ViT-Bert_Mimic
- SGLang
How to use ChayanM/ViT-Bert_Mimic with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "ChayanM/ViT-Bert_Mimic" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ChayanM/ViT-Bert_Mimic", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "ChayanM/ViT-Bert_Mimic" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ChayanM/ViT-Bert_Mimic", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ChayanM/ViT-Bert_Mimic with Docker Model Runner:
docker model run hf.co/ChayanM/ViT-Bert_Mimic
Model save
Browse files- README.md +9 -9
- generation_config.json +1 -0
README.md
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
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| No log | 1.0 | 125 | 0.
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| No log | 2.0 | 250 | 0.
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| No log | 3.0 | 375 | 0.
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### Framework versions
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1679
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- Rouge1: 33.2921
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- Rouge2: 26.4362
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- Rougel: 33.3429
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- Rougelsum: 33.528
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- Gen Len: 15.06
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
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| No log | 1.0 | 125 | 0.2445 | 0.0 | 0.0 | 0.0 | 0.0 | 200.0 |
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| No log | 2.0 | 250 | 0.1816 | 33.2921 | 26.4362 | 33.3429 | 33.528 | 12.04 |
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| No log | 3.0 | 375 | 0.1679 | 33.2921 | 26.4362 | 33.3429 | 33.528 | 15.06 |
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### Framework versions
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generation_config.json
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{
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"decoder_start_token_id": 101,
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"eos_token_id": 102,
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"pad_token_id": 0,
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"transformers_version": "4.37.1"
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}
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{
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"decoder_start_token_id": 101,
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"eos_token_id": 102,
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"max_length": 200,
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"pad_token_id": 0,
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"transformers_version": "4.37.1"
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}
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