Text Generation
Transformers
Safetensors
French
gemma
MICROBOOK
story-review
text-generation-inference
Instructions to use Finisha-F-scratch/microBook with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Finisha-F-scratch/microBook with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Finisha-F-scratch/microBook")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Finisha-F-scratch/microBook") model = AutoModelForCausalLM.from_pretrained("Finisha-F-scratch/microBook") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Finisha-F-scratch/microBook with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Finisha-F-scratch/microBook" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Finisha-F-scratch/microBook", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Finisha-F-scratch/microBook
- SGLang
How to use Finisha-F-scratch/microBook 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 "Finisha-F-scratch/microBook" \ --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": "Finisha-F-scratch/microBook", "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 "Finisha-F-scratch/microBook" \ --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": "Finisha-F-scratch/microBook", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Finisha-F-scratch/microBook with Docker Model Runner:
docker model run hf.co/Finisha-F-scratch/microBook
Training in progress, step 500
Browse files- config.json +31 -0
- generation_config.json +10 -0
- model.safetensors +3 -0
- training_args.bin +3 -0
config.json
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{
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"architectures": [
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"GemmaForCausalLM"
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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": 2,
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"dtype": "float32",
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"eos_token_id": 1,
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"head_dim": 64,
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"hidden_act": "gelu_pytorch_tanh",
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"hidden_size": 512,
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"initializer_range": 0.02,
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"intermediate_size": 1024,
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"max_position_embeddings": 1024,
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"model_type": "gemma",
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"num_attention_heads": 8,
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"num_hidden_layers": 8,
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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-06,
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"rope_parameters": {
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"rope_theta": 10000.0,
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"rope_type": "default"
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},
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"tie_word_embeddings": true,
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"transformers_version": "5.0.0",
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"use_bidirectional_attention": null,
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"use_cache": false,
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"vocab_size": 30000
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 2,
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"eos_token_id": 1,
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"output_attentions": false,
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"output_hidden_states": false,
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"pad_token_id": 0,
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"transformers_version": "5.0.0",
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"use_cache": true
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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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oid sha256:d650e2bb97c596abf648bbd4b5e47282b25b07857023df9ddd1a8405bae3b42e
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size 130688952
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:a0369eb945018b61055a0f560ed40d968c4bdc0454687d489e7d009833261587
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size 5201
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