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18aa43744d |
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@ -8,7 +8,7 @@ Notable features:
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- norm after token embedding
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- no learnable params in rmsnorm
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- no bias in linear layers
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- Multi-Query Attention (MQA) support for more efficient inference
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- Group-Query Attention (GQA) support for more efficient inference
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"""
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import math
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@ -29,7 +29,7 @@ class GPTConfig:
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vocab_size: int = 50304
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n_layer: int = 12
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n_head: int = 6 # number of query heads
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n_kv_head: int = 6 # number of key/value heads (MQA)
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n_kv_head: int = 6 # number of key/value heads (GQA)
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n_embd: int = 768
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@ -9,7 +9,6 @@ export OMP_NUM_THREADS=1
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export NANOCHAT_BASE_DIR="$HOME/.cache/nanochat"
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mkdir -p $NANOCHAT_BASE_DIR
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command -v uv &> /dev/null || curl -LsSf https://astral.sh/uv/install.sh | sh
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[ -d ".venv" ] || uv venv
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uv sync --extra gpu
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source .venv/bin/activate
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if [ -z "$WANDB_RUN" ]; then
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4
speedrun.sh
Normal file → Executable file
4
speedrun.sh
Normal file → Executable file
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@ -20,9 +20,7 @@ mkdir -p $NANOCHAT_BASE_DIR
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# install uv (if not already installed)
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command -v uv &> /dev/null || curl -LsSf https://astral.sh/uv/install.sh | sh
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# create a .venv local virtual environment (if it doesn't exist)
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[ -d ".venv" ] || uv venv
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# install the repo dependencies
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# create a .venv local virtual environment (if it doesn't exist) and install the repo dependencies
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uv sync --extra gpu
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# activate venv so that `python` uses the project's venv instead of system python
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source .venv/bin/activate
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