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--- |
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library_name: peft |
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base_model: mistralai/Mistral-7B-Instruct-v0.1 |
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datasets: |
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- bipulai/skillate_helpdesk |
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language: |
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- en |
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tags: |
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- fine_tuning |
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- customer_support |
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- mistral |
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- skillate |
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- Text Generation |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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This is the fine tuned model which got further trained on the top of base model Mistral-7B-v0.1 on the Skillate customer support dataset. |
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The fine-tuned model understands the nuances about how the Skillate product works, its navigation, features, monologue and respond accordingly. |
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## Instruction format |
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In order to leverage instruction fine-tuning, your prompt should be surrounded by [INST] and [/INST] tokens. |
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## How to Get Started with the Model |
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from transformers import AutoTokenizer,AutoModelForCausalLM, BitsAndBytesConfig |
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quantization_config = BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_quant_type="nf4", |
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bnb_4bit_use_double_quant=True, |
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bnb_4bit_compute_dtype=bfloat16 |
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) |
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tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") |
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tokenizer.pad_token = tokenizer.eos_token |
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base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1",device_map="auto",quantization_config=quantization_config) |
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peft_model = PeftModel.from_pretrained(base_model, "bipulai/mistral-7b-v1-skillate-helpdesk",device_map="auto") |
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peft_model.merge_and_unload() |
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tokenize = tokenizer(text = [prompt],return_tensors = "pt") |
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x = peft_model.generate(input_ids = tokenize["input_ids"].to(device),attention_mask = tokenize["attention_mask"].to(device),max_length = 500) |
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response = tokenizer.batch_decode(x,skip_special_tokens=True) |
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print(f"Model Output: {reponse}\n\n") |
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