Instructions to use if001/gemma_tiny_expand with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use if001/gemma_tiny_expand with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="if001/gemma_tiny_expand")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("if001/gemma_tiny_expand") model = AutoModelForCausalLM.from_pretrained("if001/gemma_tiny_expand") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use if001/gemma_tiny_expand with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "if001/gemma_tiny_expand" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "if001/gemma_tiny_expand", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/if001/gemma_tiny_expand
- SGLang
How to use if001/gemma_tiny_expand 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 "if001/gemma_tiny_expand" \ --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": "if001/gemma_tiny_expand", "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 "if001/gemma_tiny_expand" \ --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": "if001/gemma_tiny_expand", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use if001/gemma_tiny_expand with Docker Model Runner:
docker model run hf.co/if001/gemma_tiny_expand
Upload GemmaForCausalLM
Browse files- config.json +5 -4
- generation_config.json +2 -2
- model.safetensors +2 -2
config.json
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id":
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"eos_token_id":
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"head_dim": 256,
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"hidden_act": "gelu_pytorch_tanh",
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"hidden_activation": "gelu_pytorch_tanh",
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"max_position_embeddings": 1024,
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"model_type": "gemma",
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"num_attention_heads": 4,
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"num_hidden_layers":
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"num_key_value_heads": 1,
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"pad_token_id": 0,
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"rms_norm_eps": 1e-
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"rope_theta": 10000.0,
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"torch_dtype": "float32",
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"transformers_version": "4.41.2",
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"use_cache": true,
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 7,
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"head_dim": 256,
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"hidden_act": "gelu_pytorch_tanh",
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"hidden_activation": "gelu_pytorch_tanh",
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"max_position_embeddings": 1024,
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"model_type": "gemma",
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"num_attention_heads": 4,
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"num_hidden_layers": 2,
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"num_key_value_heads": 1,
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"pad_token_id": 0,
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"rms_norm_eps": 1e-05,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "float32",
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"transformers_version": "4.41.2",
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"use_cache": true,
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generation_config.json
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"transformers_version": "4.41.2"
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}
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 7,
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"pad_token_id": 0,
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"transformers_version": "4.41.2"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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size 915429664
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