Text Generation
Transformers
PyTorch
Safetensors
English
llama
Eval Results
text-generation-inference
Inference Endpoints
Pankaj Mathur commited on
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Update README.md

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@@ -18,7 +18,7 @@ We build explain tuned [WizardLM dataset ~70K](https://github.com/nlpxucan/Wizar
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  We leverage all of the 15 system instructions provided in Orca Research Paper. to generate custom datasets, in contrast to vanilla instruction tuning approaches used by original datasets.
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- This helps student model aka [wizardlm_alpaca_dolly_orca_open_llama_13b](https://huggingface.co/psmathur/wizardlm_alpaca_dolly_orca_open_llama_13b) to learn ***thought*** process from teacher model, which is ChatGPT (gpt-3.5-turbo-0301 version).
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  Please see below example usage how the **System** prompt is added before each **instruction**.
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@@ -53,7 +53,7 @@ import torch
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  from transformers import LlamaForCausalLM, LlamaTokenizer
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  # Hugging Face model_path
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- model_path = 'psmathur/wizardlm_alpaca_dolly_orca_open_llama_13b'
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  tokenizer = LlamaTokenizer.from_pretrained(model_path)
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  model = LlamaForCausalLM.from_pretrained(
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  model_path, torch_dtype=torch.float16, device_map='auto',
 
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  We leverage all of the 15 system instructions provided in Orca Research Paper. to generate custom datasets, in contrast to vanilla instruction tuning approaches used by original datasets.
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+ This helps student model aka this model to learn ***thought*** process from teacher model, which is ChatGPT (gpt-3.5-turbo-0301 version).
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  Please see below example usage how the **System** prompt is added before each **instruction**.
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  from transformers import LlamaForCausalLM, LlamaTokenizer
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  # Hugging Face model_path
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+ model_path = 'psmathur/orca_mini_13b'
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  tokenizer = LlamaTokenizer.from_pretrained(model_path)
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  model = LlamaForCausalLM.from_pretrained(
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  model_path, torch_dtype=torch.float16, device_map='auto',