Update app.py

app.py

@@ -1,3 +1,99 @@
 import gradio as gr
 
-gr.Interface.load("models/AlexWortega/instruct_rugptlarge").launch()
+
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+
+from transformers import GPT2TokenizerFast,GPT2LMHeadModel
+tokenizer = GPT2TokenizerFast.from_pretrained("AlexWortega/instruct_rugptlarge")
+special_tokens_dict = {'additional_special_tokens': ['<code>', '</code>', '<instructionS>', '<instructionE>', '<next>']}
+
+tokenizer.add_special_tokens(special_tokens_dict)
+device = 'cuda' # мэх дорога
+model = GPT2LMHeadModel.from_pretrained("AlexWortega/instruct_rugptlarge")
+#
+
+model.resize_token_embeddings(len(tokenizer))
+
+def generate_prompt(instruction, input=None):
+    if input:
+        return f"{input}:"
+    return f"{instruction}"
+
+def generate_seqs(q, temp, topp, topk, nb, maxtok):
+    k=1
+    gen_kwargs = {
+        "min_length": 20,
+        "max_new_tokens": maxtok,
+        "top_k": topk,
+        "top_p": topp,
+        "do_sample": True,  
+        "early_stopping": True,
+        "no_repeat_ngram_size": 2,
+        "temperature":temp,
+        
+        "eos_token_id": tokenizer.eos_token_id,
+        "pad_token_id": tokenizer.eos_token_id,
+        "use_cache": True,
+        "repetition_penalty": 1.5,  
+        "length_penalty": 0.8,  
+        "num_beams": nb,
+        "num_return_sequences": k
+    }
+    if len(q)>0:
+        q = q + '<instructionS>'
+    else:
+        q = 'Как зарабатывать денег на нейросетях?' + '<instructionS>'
+    t = tokenizer.encode(q, return_tensors='pt').to(device)
+    g = model.generate(t, **gen_kwargs)
+    generated_sequences = tokenizer.batch_decode(g, skip_special_tokens=False)
+    #print(generated_sequences)
+    # Add </s></s>A: after the question and before each generated sequence
+    #sequences = [f"H:{q}</s></s>A:{s.replace(q, '')}" for s in generated_sequences]
+
+    # Compute the reward score for each generated sequence
+    #cores = [reward_model.reward_score(q, s.split('</s></s>A:')[-1]) for s in sequences]
+
+    # Return the k sequences with the highest score and their corresponding scores
+    # results = [(s, score) for score, s in sorted(zip(scores, sequences), reverse=True)[:k]]
+    ans = generated_sequences[0].replace('<instructionS>','\n').replace('<instructionE>','').replace('<|endoftext|>','')
+    return ans
+
+description_html = '''
+<p>Обучена на 2v100, коллективом авторов:</p>
+<ul>
+    <li><a href="https://t.me/YallenGusev" target="_blank">@YallenGusev</a></li>
+    <li><a href="https://t.me/lovedeathtransformers" target="_blank">@lovedeathtransformers</a></li>
+    <li><a href="https://t.me/alexkuk" target="_blank">@alexkuk</a></li>
+    <li><a href="https://t.me/chckdskeasfsd" target="_blank">@chckdskeasfsd</a></li>
+    <li><a href="https://t.me/dno5iq" target="_blank">@dno5iq</a></li>
+</ul>
+'''
+
+g = gr.Interface(
+    fn=generate_seqs,
+    inputs=[
+        gr.components.Textbox(
+            lines=2, label="Впишите сюда задачу, а я попробую решить", placeholder="Как зарабатывать денег на нейросетях?"
+        ),
+        #gr.components.Textbox(lines=2, label="Вход", placeholder="Нет"),
+        gr.components.Slider(minimum=0.1, maximum=2, value=1.0, label="Temperature"),
+        gr.components.Slider(minimum=0, maximum=1, value=0.9, label="Top p"),
+        gr.components.Slider(minimum=0, maximum=100, value=50, label="Top k"),
+        gr.components.Slider(minimum=0, maximum=5, step=1, value=4, label="Beams"),
+        gr.components.Slider(
+            minimum=1, maximum=256, step=1, value=100, label="Max tokens"
+        ),
+    ],
+    outputs=[
+        gr.inputs.Textbox(
+            lines=5,
+            label="Output",
+        )
+    ],
+    title="ruInstructlarge",
+    description=description_html)
+
+
+g.queue(concurrency_count=5)
+g.launch(share=True)