Instructions to use kaanino/gpt-mha-SwiGLU with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kaanino/gpt-mha-SwiGLU with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import GPT model = GPT.from_pretrained("kaanino/gpt-mha-SwiGLU", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 107
Browse files- config.json +19 -0
- model.safetensors +3 -0
- training_args.bin +3 -0
config.json
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{
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"activation_type": "swiglu",
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"architectures": [
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"GPT"
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],
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"dropout": 0.1,
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"dtype": "float32",
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"max_seq_length": 128,
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"model_type": "gpt",
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"n_embd": 64,
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"n_head": 4,
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"n_kv_head": 4,
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"n_layer": 6,
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"norm_type": "rmsnorm",
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"pos_enc_type": "relative",
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"transformers_version": "5.5.4",
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"use_cache": false,
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"vocab_size": 50257
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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:eec13bccd1300bfedc73056d8586983f6cd18071dc7de70225cde447f9e90837
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size 27411240
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:3e3ef995a45a005dd25dc91fc3c0f1a51673148551c4d991553dd1502debc1c0
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size 5201
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