Instructions to use fine-tune/test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fine-tune/test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="fine-tune/test")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("fine-tune/test") model = AutoModelForMultimodalLM.from_pretrained("fine-tune/test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use fine-tune/test with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "fine-tune/test" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "fine-tune/test", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/fine-tune/test
- SGLang
How to use fine-tune/test 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 "fine-tune/test" \ --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": "fine-tune/test", "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 "fine-tune/test" \ --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": "fine-tune/test", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use fine-tune/test with Docker Model Runner:
docker model run hf.co/fine-tune/test
Training in progress, epoch 0
Browse files- config.json +5 -5
- model.safetensors +2 -2
config.json
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"is_decoder": true,
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"is_encoder_decoder": false,
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"max_length": 768,
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"max_position_embeddings":
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"model_type": "mbart",
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"num_hidden_layers": 12,
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"scale_embedding": true,
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"torch_dtype": "float32",
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"use_cache": true,
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"vocab_size":
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},
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"decoder_start_token_id":
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"encoder": {
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"attention_probs_dropout_prob": 0.0,
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"depths": [
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"hidden_dropout_prob": 0.0,
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"hidden_size": 1024,
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"image_size": [
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],
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"initializer_range": 0.02,
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"layer_norm_eps": 1e-05,
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"is_decoder": true,
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"is_encoder_decoder": false,
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"max_length": 768,
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"max_position_embeddings": 768,
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"model_type": "mbart",
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"num_hidden_layers": 12,
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"scale_embedding": true,
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"torch_dtype": "float32",
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"use_cache": true,
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"vocab_size": 57604
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},
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"decoder_start_token_id": 57579,
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"encoder": {
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"attention_probs_dropout_prob": 0.0,
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"depths": [
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"hidden_dropout_prob": 0.0,
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"hidden_size": 1024,
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"image_size": [
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800,
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],
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"initializer_range": 0.02,
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"layer_norm_eps": 1e-05,
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:953fb7774e853f0e315927440a4bb350798501e8dfa96912d16f88738e7809aa
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size 806248600
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