mirror of
https://github.com/xai-org/grok-1
synced 2024-11-15 14:25:22 +08:00
56 lines
1.4 KiB
Python
56 lines
1.4 KiB
Python
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CKPT_PATH = "./checkpoints/"
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def main():
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_1_model = LanguageModelConfig(
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vocab_size=128 * 1024,
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pad_token=0,
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eos_token=2,
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sequence_len=8192,
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embedding_init_scale=,
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output_multiplier_scale=0.5773502691896257,
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embedding_multiplier_scale=78.38367176906169,
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model=TransformerConfig(
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emb_size=48 * 128,
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widening_factor=8,
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key_size=128,
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num_q_heads=48,
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num_kv_heads=8,
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num_layers=64,
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attn_output_multiplier=0.08838834764831845,
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shard_activations=True,
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# MoE.
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num_experts=8,
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num_selected_experts=2,
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# Activation sharding.
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data_axis="data",
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model_axis="model",
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),
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)
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inference_runner = InferenceRunner(
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pad_sizes=(1024,),
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runner=ModelRunner(
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mode_model,
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bs_per_device=0.125,
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checkpoint_path=CKPT_PATH,
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),
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name="local",
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load=CKPT_PATH,
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tokenizer_path="./tokenizer.model",
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local_mesh_config=(1, 8),
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_config=(1, 1),
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)
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inference_runner.initialize()
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gen = inference_runner.run()
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inp = course"
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print(f"Output for prompt: {inp}", sample_from_model(, inp, max_len=100, temperature=0.01))
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if __name__ == "__main__":
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logging.basicConfig(level=logging.INFO)
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main()
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