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| #import torch | |
| #from transformers import AutoTokenizer, AutoModelForCausalLM | |
| from langchain_community.llms import HuggingFaceHub | |
| from langchain_community.llms import HuggingFaceTextGenInference | |
| # Load your Telugu model | |
| """ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| model_name = "Telugu-LLM-Labs/Telugu-Llama2-7B-v0-Instruct" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16).to(device) """ | |
| ENDPOINT_URL = "https://api-inference.huggingface.co/models/Telugu-LLM-Labs/Telugu-Llama2-7B-v0-Instruct" | |
| HF_TOKEN = os.getenv("huggingface_token") | |
| llm = HuggingFaceTextGenInference( | |
| inference_server_url=ENDPOINT_URL, | |
| max_new_tokens=512, | |
| top_k=50, | |
| temperature=0.1, | |
| repetition_penalty=1.03, | |
| server_kwargs={ | |
| "headers": { | |
| "Authorization": f"Bearer {HF_TOKEN}", | |
| "Content-Type": "application/json", | |
| } | |
| }, | |
| ) | |
| def summarize(text, llm): | |
| instruction = "కింది వచనాన్ని సంగ్రహించండి: " | |
| prompt = instruction + text | |
| response = llm(prompt) | |
| return response | |
| input_text = "గూగుల్ వార్తలు అనేది గూగుల్ ద్వారా అభివృద్ధి చేయబడిన వార్తా అగ్రిగేటర్ సేవ..." | |
| result = summarize(input_text, llm) | |
| print(result) |