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Alternative weight loading via .safetensors (#507)

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Sebastian Raschka
2025-01-29 08:15:29 -06:00
committed by GitHub
parent 9daa7e7511
commit 25ea71e713
6 changed files with 336 additions and 6 deletions
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17
ch05/01_main-chapter-code/ch05.ipynb
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@@ -2103,7 +2103,20 @@
"id": "127ddbdb-3878-4669-9a39-d231fbdfb834",
"metadata": {},
"source": [
"- For an alternative way to load the weights from the Hugging Face Hub, see [../02_alternative_weight_loading](../02_alternative_weight_loading)"
"<span style=\"color:darkred\">\n",
" <ul>\n",
" <li>For an alternative way to load the weights from the Hugging Face Hub, see <a href=\"../02_alternative_weight_loading\">../02_alternative_weight_loading</a></li>\n",
" <ul>\n",
" <li>This is useful if:</li>\n",
" <ul>\n",
" <li>the weights are temporarily unavailable</li>\n",
" <li>a company VPN only permits downloads from the Hugging Face Hub but not from the OpenAI CDN, for example</li>\n",
" <li>you are having trouble with the TensorFlow installation (the original weights are stored in TensorFlow files)</li>\n",
" </ul>\n",
" </ul>\n",
" <li>The <a href=\"../02_alternative_weight_loading\">../02_alternative_weight_loading</a> code notebooks are replacements for the remainder of this section 5.5</li>\n",
" </ul>\n",
"</span>\n"
]
},
{
@@ -2505,7 +2518,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.6"
"version": "3.11.4"
}
},
"nbformat": 4,

6
ch05/01_main-chapter-code/gpt_generate.py
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@@ -155,8 +155,8 @@ def assign(left, right):
def load_weights_into_gpt(gpt, params):
gpt.pos_emb.weight = assign(gpt.pos_emb.weight, params['wpe'])
gpt.tok_emb.weight = assign(gpt.tok_emb.weight, params['wte'])
gpt.pos_emb.weight = assign(gpt.pos_emb.weight, params["wpe"])
gpt.tok_emb.weight = assign(gpt.tok_emb.weight, params["wte"])
for b in range(len(params["blocks"])):
q_w, k_w, v_w = np.split(
@@ -229,7 +229,7 @@ def generate(model, idx, max_new_tokens, context_size, temperature=0.0, top_k=No
# Keep only top_k values
top_logits, _ = torch.topk(logits, top_k)
min_val = top_logits[:, -1]
logits = torch.where(logits < min_val, torch.tensor(float('-inf')).to(logits.device), logits)
logits = torch.where(logits < min_val, torch.tensor(float("-inf")).to(logits.device), logits)
# New: Apply temperature scaling
if temperature > 0.0: