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Browse files
app.py
CHANGED
@@ -14,6 +14,7 @@ MOCK = False
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TEST_FOLDER = "c4f5"
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MODEL_NAME="xu3kev/deepseekcoder-7b-logo-pbe"
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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hug_model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.float16,).to('cuda')
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@@ -222,7 +223,7 @@ def llm_call(question_prompt, model_name,
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top_p=1, n_samples=64, stop=None):
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if HUGGINGFACE:
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model_inputs = hug_tokenizer([question_prompt], return_tensors="pt").to('cuda')
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generated_ids = hug_model.generate(**model_inputs, max_length=1400, temperature=1, num_return_sequences=
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responses = hug_tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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codes = []
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for response in responses:
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@@ -420,18 +421,18 @@ def main():
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output_image = gr.Image(label="output")
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submit_button.click(img_to_code_img, inputs=canvas, outputs=output_image)
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demo.launch(share=True)
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TEST_FOLDER = "c4f5"
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MODEL_NAME="xu3kev/deepseekcoder-7b-logo-pbe"
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MODEL_NAME="microsoft/Phi-3-small-8k-instruct"
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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hug_model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.float16,).to('cuda')
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top_p=1, n_samples=64, stop=None):
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if HUGGINGFACE:
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model_inputs = hug_tokenizer([question_prompt], return_tensors="pt").to('cuda')
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generated_ids = hug_model.generate(**model_inputs, max_length=1400, temperature=1, num_return_sequences=1, do_sample=True)
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responses = hug_tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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codes = []
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for response in responses:
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output_image = gr.Image(label="output")
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submit_button.click(img_to_code_img, inputs=canvas, outputs=output_image)
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# demo.load(
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# None,
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# None,
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# js="""
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# () => {
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# const params = new URLSearchParams(window.location.search);
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# if (!params.has('__theme')) {
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# params.set('__theme', 'light');
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# window.location.search = params.toString();
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# }
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# }""",
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# )
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demo.launch(share=True)
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