abhi-mosaic
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Update README.md
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README.md
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@@ -67,7 +67,10 @@ This model is best used with the MosaicML [llm-foundry repository](https://githu
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```python
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import transformers
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model = transformers.AutoModelForCausalLM.from_pretrained(
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```
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Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method.
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This is because we use a custom `MPT` model architecture that is not yet part of the Hugging Face `transformers` package.
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To use the optimized [triton implementation](https://github.com/openai/triton) of FlashAttention, you can load the model with `attn_impl='triton'` and move the model to `bfloat16`:
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```python
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config = transformers.AutoConfig.from_pretrained(
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config.attn_config['attn_impl'] = 'triton'
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model = transformers.AutoModelForCausalLM.from_pretrained(
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model.to(device='cuda:0')
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```
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Although the model was trained with a sequence length of 2048, ALiBi enables users to increase the maximum sequence length during finetuning and/or inference. For example:
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```python
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config = transformers.AutoConfig.from_pretrained(
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config.update({"max_seq_len": 4096})
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model = transformers.AutoModelForCausalLM.from_pretrained(
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```
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This model was trained with the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer.
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```python
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import transformers
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model = transformers.AutoModelForCausalLM.from_pretrained(
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'mosaicml/mpt-7b-chat',
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trust_remote_code=True
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)
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```
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Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method.
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This is because we use a custom `MPT` model architecture that is not yet part of the Hugging Face `transformers` package.
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To use the optimized [triton implementation](https://github.com/openai/triton) of FlashAttention, you can load the model with `attn_impl='triton'` and move the model to `bfloat16`:
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```python
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config = transformers.AutoConfig.from_pretrained(
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'mosaicml/mpt-7b-chat',
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trust_remote_code=True
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)
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config.attn_config['attn_impl'] = 'triton'
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model = transformers.AutoModelForCausalLM.from_pretrained(
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'mosaicml/mpt-7b-chat',
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config=config,
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torch_dtype=torch.bfloat16,
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trust_remote_code=True
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)
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model.to(device='cuda:0')
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```
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Although the model was trained with a sequence length of 2048, ALiBi enables users to increase the maximum sequence length during finetuning and/or inference. For example:
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```python
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config = transformers.AutoConfig.from_pretrained(
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'mosaicml/mpt-7b-chat',
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trust_remote_code=True
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)
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config.update({"max_seq_len": 4096})
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model = transformers.AutoModelForCausalLM.from_pretrained(
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'mosaicml/mpt-7b-chat',
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config=config,
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trust_remote_code=True
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)
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```
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This model was trained with the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer.
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