Uploaded model

Usage model.

import gradio as gr
from unsloth import FastLanguageModel

# Load your pre-trained model
max_seq_length = 2048
dtype = None
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
 model_name="blackhole33/llama-3-8b-bnb-4bit",
 max_seq_length=max_seq_length,
 dtype=dtype,
 load_in_4bit=load_in_4bit,
)

FastLanguageModel.for_inference(model)  # Enable native 2x faster inference

# Alpaca prompt template
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.

### Instruction:
{}

### Response:
{}"""

# Function to generate response
def generate_response(instruction):
 inputs = tokenizer(
     [
         alpaca_prompt.format(
             instruction,  # instruction
             ""  # output - leave this blank for generation!
         )
     ],
     return_tensors="pt",
 ).to("cuda")

 outputs = model.generate(**inputs, max_new_tokens=250, use_cache=True)
 res = tokenizer.batch_decode(outputs, skip_special_tokens=True)
 return res[0]

# Gradio interface
interface = gr.Interface(
 fn=generate_response,
 inputs=[
     gr.Textbox(lines=2, placeholder="Question"),
 ],
 outputs="text",
 title="Uzbek Language Model Interface",
 description="Enter an instruction and context to get a response from the model.",
)

# Launch the interface
interface.launch(share=True)
Developed by: blackhole33
License: apache-2.0
Finetuned from model : llama-3-8b-bnb-4bit