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Model: Locutusque/gpt2-large-conversational Source: Original Platform
2026-07-20 19:00:13 +08:00
commit 5e3b6655e9
13 changed files with 150590 additions and 0 deletions
--- a/run_model.py
+++ b/run_model.py
@@ -0,0 +1,38 @@
+import torch
+from transformers import GPT2Tokenizer, AutoModelForCausalLM
+start_token = "<|ASSISTANT|>"
+end_token = "<|"
+tokenizer = GPT2Tokenizer.from_pretrained('gpt2-large')
+model = AutoModelForCausalLM.from_pretrained('gpt2-large', torch_dtype=torch.bfloat16)
+tokenizer.pad_token = "[PAD]"
+tokenizer.eos_token = "<|endoftext|>"
+tokenizer.add_special_tokens({"additional_special_tokens": ["<|ASSISTANT|>", "<|USER|>", "<|SYSTEM|>"]})
+model.resize_token_embeddings(len(tokenizer))
+model.load_state_dict(torch.load("/media/locutusque/T7/Projects/results/pytorch_model.bin"))
+model.cuda()
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+def generate_text(model, tokenizer, prompt, max_length=1024):
+    prompt = f'<|USER|> {prompt} <|ASSISTANT|> '
+    input_ids = tokenizer.encode(prompt, add_special_tokens=True, return_tensors="pt").to(device)
+    attention_mask = torch.ones_like(input_ids).to(device)
+    output = model.generate(input_ids, 
+                            max_length=max_length, 
+                            do_sample=True, 
+                            top_k=0, 
+                            top_p=0.1,
+                            temperature=0.75,
+                            repetition_penalty=1.176,
+                            pad_token_id=tokenizer.pad_token_id,
+                            eos_token_id=tokenizer.eos_token_id,
+                            attention_mask=attention_mask)
+    output_ids = tokenizer.decode(output[0], skip_special_tokens=False)
+    return output_ids
+# Loop to interact with the model
+while True:
+    prompt = input("Enter a prompt (or 'q' to quit): ")
+    if prompt == "q":
+        break
+    output_text = generate_text(model, tokenizer, prompt)
+    text_between_tokens = output_text[output_text.find(start_token) + len(start_token):]
+    out = text_between_tokens[:text_between_tokens.find(end_token)]
+    print(out)