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--- |
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language: |
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- uz |
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license: apache-2.0 |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- llama |
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- trl |
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base_model: llama-3-8b-bnb-4bit |
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--- |
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# Uploaded model |
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# Usage model. |
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``` |
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import gradio as gr |
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from unsloth import FastLanguageModel |
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# Load your pre-trained model |
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max_seq_length = 2048 |
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dtype = None |
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load_in_4bit = True |
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model, tokenizer = FastLanguageModel.from_pretrained( |
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model_name="blackhole33/llama-3-8b-bnb-4bit", |
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max_seq_length=max_seq_length, |
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dtype=dtype, |
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load_in_4bit=load_in_4bit, |
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) |
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FastLanguageModel.for_inference(model) # Enable native 2x faster inference |
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# Alpaca prompt template |
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alpaca_prompt = """Quyida vazifani tavsiflovchi ko'rsatma mavjud bo'lib, u qo'shimcha kontekstni ta'minlaydigan kiritish bilan bog'langan. So'rovni to'g'ri to'ldiradigan javob yozing. |
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### Instruction: |
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{} |
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### Response: |
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{}""" |
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# Function to generate response |
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def generate_response(instruction): |
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inputs = tokenizer( |
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[ |
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alpaca_prompt.format( |
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instruction, # instruction |
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"" # output - leave this blank for generation! |
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) |
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], |
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return_tensors="pt", |
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).to("cuda") |
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outputs = model.generate(**inputs, max_new_tokens=250, use_cache=True) |
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res = tokenizer.batch_decode(outputs, skip_special_tokens=True) |
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return res[0] |
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# Gradio interface |
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interface = gr.Interface( |
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fn=generate_response, |
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inputs=[ |
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gr.Textbox(lines=2, placeholder="Question"), |
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], |
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outputs="text", |
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title="Uzbek Language Model Interface", |
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description="Enter an instruction and context to get a response from the model.", |
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) |
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# Launch the interface |
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interface.launch(share=True) |
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``` |
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- **Developed by:** blackhole33 |
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- **License:** apache-2.0 |
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- **Finetuned from model :** llama-3-8b-bnb-4bit |
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