Update README.md
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README.md
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@@ -82,13 +82,13 @@ from h2oai_pipeline import H2OTextGenerationPipeline
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(
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"PAIXAI/Astrid-Mistral-7B,
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use_fast=True,
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padding_side="left",
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trust_remote_code=True,
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)
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model = AutoModelForCausalLM.from_pretrained(
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"PAIXAI/Astrid-Mistral-7B,
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torch_dtype="auto",
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device_map={"": "cuda:0"},
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trust_remote_code=True,
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@@ -114,7 +114,7 @@ You may also construct the pipeline from the loaded model and tokenizer yourself
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "PAIXAI/Astrid-Mistral-7B
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# Important: The prompt needs to be in the same format the model was trained with.
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# You can find an example prompt in the experiment logs.
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prompt = "<|prompt|>How are you?<|im_end|><|answer|>"
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(
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"PAIXAI/Astrid-Mistral-7B",
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use_fast=True,
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padding_side="left",
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trust_remote_code=True,
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)
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model = AutoModelForCausalLM.from_pretrained(
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"PAIXAI/Astrid-Mistral-7B",
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torch_dtype="auto",
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device_map={"": "cuda:0"},
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trust_remote_code=True,
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "PAIXAI/Astrid-Mistral-7B" # either local folder or huggingface model name
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# Important: The prompt needs to be in the same format the model was trained with.
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# You can find an example prompt in the experiment logs.
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prompt = "<|prompt|>How are you?<|im_end|><|answer|>"
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