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switch back to 125M
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app.py
CHANGED
@@ -2,7 +2,8 @@ import gradio as gr
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "flax-community/gpt-code-clippy-1.3B-apps-alldata
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model = AutoModelForCausalLM.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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tokenizer.pad_token = tokenizer.eos_token
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@@ -39,14 +40,14 @@ def generate_solution(question, starter_code="", temperature=1., num_beams=1):
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top_p=0.95,
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pad_token_id=tokenizer.pad_token_id,
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early_stopping=True,
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temperature=
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num_beams=int(num_beams),
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no_repeat_ngram_size=None,
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repetition_penalty=None,
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num_return_sequences=None,
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)
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return format_outputs(tokenizer.decode(output[0][start:]).strip())
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_EXAMPLES = [
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# model_name = "flax-community/gpt-code-clippy-1.3B-apps-alldata"
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model_name = "flax-community/gpt-code-clippy-125M-apps-alldata"
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model = AutoModelForCausalLM.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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tokenizer.pad_token = tokenizer.eos_token
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top_p=0.95,
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pad_token_id=tokenizer.pad_token_id,
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early_stopping=True,
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temperature=temperature,
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num_beams=int(num_beams),
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no_repeat_ngram_size=None,
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repetition_penalty=None,
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num_return_sequences=None,
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)
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return format_outputs(tokenizer.decode(output[0][start:], skip_special_tokens=True).strip())
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_EXAMPLES = [
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