初始化项目，由ModelHub XC社区提供模型
Model: IAmSkyDra/GemSUra-edu Source: Original Platform
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config.json
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README.md
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README.md
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---
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license: apache-2.0
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datasets:
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- IAmSkyDra/HCMUT_FAQ
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language:
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- vi
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tags:
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- education
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- text-generation-inference
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- gemma
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- llama-factory
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- unsloth
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widget:
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- text: Chào bạn
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output:
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text: >-
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Chào bạn! Tôi là GemSUra-edu, một trợ lý AI được phát triển bởi Long
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Nguyen.
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example_title: Query 1
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- text: Hiệu trưởng hiện tại của trường Đại học Bách Khoa
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output:
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text: >-
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Hiệu trưởng hiện tại của trường Đại học Bách Khoa là PGS. TS. Mai Thanh Phong.
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example_title: Query 2
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- text: OISP là viết tắt của
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output:
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text: >-
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Văn phòng Đào tạo Quốc tế (Office for International Study Programs)
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example_title: Query 3
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---
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## Introduction
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GemSUra-edu is a large language model fine-tuned on a dataset of FAQs from HCMUT, based on the pre-trained model [GemSUra 2B](https://huggingface.co/ura-hcmut/GemSUra-2B) developed by the URA research group at Ho Chi Minh City University of Technology (HCMUT).
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## Inference (with Unsloth for higher speed)
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```python
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from unsloth import FastLanguageModel
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import torch
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# Load model and tokenizer
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name="IAmSkyDra/GemSUra-edu",
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max_seq_length=4096,
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dtype=None,
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load_in_4bit=True
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)
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FastLanguageModel.for_inference(model)
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query_template = "<start_of_turn>user\n{query}<end_of_turn>\n<start_of_turn>model\n"
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while True:
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query = input("Query: ")
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if query.lower() == "exit":
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break
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query = query_template.format(query=query)
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inputs = tokenizer(query, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=4096, use_cache=True)
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generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
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answer = generated_text[0].split("model\n")[1].strip()
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print(answer)
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```
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## Inference (with Transformers)
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```python
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import transformers
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from transformers import AutoModelForCausalLM, AutoTokenizer
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pipeline_kwargs = {
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"temperature": 0.1,
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"max_new_tokens": 4096,
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"do_sample": True
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}
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if __name__ == "__main__":
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# Load model
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model = AutoModelForCausalLM.from_pretrained(
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"IAmSkyDra/GemSUra-edu",
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device_map="auto"
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)
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model.eval()
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(
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"IAmSkyDra/GemSUra-edu",
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trust_remote_code=True
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)
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pipeline = transformers.pipeline(
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model=model,
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tokenizer=tokenizer,
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return_full_text=False,
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task='text-generation',
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**pipeline_kwargs
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)
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query_template = "<start_of_turn>user\n{query}<end_of_turn>\n<start_of_turn>model\n"
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while True:
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query = input("Query: ")
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if query.lower() == "exit":
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break
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query = query_template.format(query=query)
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answer = pipeline(query)[0]["generated_text"]
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answer = answer.split("model\n")[1].strip()
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print(answer)
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```
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## Notation
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If you want to quantize the model for deployment on local devices, it should be quantized to at least 8 bits.
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config.json
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{
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"_name_or_path": "ura-hcmut/GemSUra-2B",
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"architectures": [
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"GemmaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 2,
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"eos_token_id": 1,
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"head_dim": 256,
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"hidden_act": "gelu",
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"hidden_activation": "gelu_pytorch_tanh",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 16384,
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"max_position_embeddings": 8192,
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"model_type": "gemma",
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"num_attention_heads": 8,
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"num_hidden_layers": 18,
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"num_key_value_heads": 1,
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"pad_token_id": 0,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.42.4",
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"use_cache": true,
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"vocab_size": 256000
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}
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34
special_tokens_map.json
Normal file
34
special_tokens_map.json
Normal file
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3
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71
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71
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3
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Reference in New Issue
Block a user