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d538293f62
* transformers-cli -> transformers * Chat command works with positional argument * update doc references to transformers-cli * doc headers * deepspeed --------- Co-authored-by: Joao Gante <joao@huggingface.co>
178 lines
9.7 KiB
Markdown
178 lines
9.7 KiB
Markdown
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">
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</div>
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</div>
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# Llama 2
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[Llama 2](https://huggingface.co/papers/2307.09288) is a family of large language models, Llama 2 and Llama 2-Chat, available in 7B, 13B, and 70B parameters. The Llama 2 model mostly keeps the same architecture as [Llama](./llama), but it is pretrained on more tokens, doubles the context length, and uses grouped-query attention (GQA) in the 70B model to improve inference.
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Llama 2-Chat is trained with supervised fine-tuning (SFT), and reinforcement learning with human feedback (RLHF) - rejection sampling and proximal policy optimization (PPO) - is applied to the fine-tuned model to align the chat model with human preferences.
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You can find all the original Llama 2 checkpoints under the [Llama 2 Family](https://huggingface.co/collections/meta-llama/llama-2-family-661da1f90a9d678b6f55773b) collection.
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> [!TIP]
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> Click on the Llama 2 models in the right sidebar for more examples of how to apply Llama to different language tasks.
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The example below demonstrates how to generate text with [`Pipeline`], [`AutoModel`], and how to chat with Llama 2-Chat from the command line.
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<hfoptions id="usage">
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<hfoption id="Pipeline">
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```py
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import torch
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from transformers import pipeline
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pipeline = pipeline(
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task="text-generation",
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model="meta-llama/Llama-2-7b-hf",
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torch_dtype=torch.float16,
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device=0
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)
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pipeline("Plants create energy through a process known as")
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```
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</hfoption>
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<hfoption id="AutoModel">
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```py
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(
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"meta-llama/Llama-2-7b-hf",
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)
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model = AutoModelForCausalLM.from_pretrained(
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"meta-llama/Llama-2-7b-hf",
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torch_dtype=torch.float16,
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device_map="auto",
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attn_implementation="sdpa"
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)
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input_ids = tokenizer("Plants create energy through a process known as", return_tensors="pt").to("cuda")
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output = model.generate(**input_ids, cache_implementation="static")
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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</hfoption>
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<hfoption id="transformers CLI">
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```bash
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transformers chat meta-llama/Llama-2-7b-chat-hf --torch_dtype auto --attn_implementation flash_attention_2
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```
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</hfoption>
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</hfoptions>
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Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.
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The example below uses [torchao](../quantization/torchao) to only quantize the weights to int4.
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```py
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# pip install torchao
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import torch
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from transformers import TorchAoConfig, AutoModelForCausalLM, AutoTokenizer
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quantization_config = TorchAoConfig("int4_weight_only", group_size=128)
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model = AutoModelForCausalLM.from_pretrained(
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"meta-llama/Llama-2-13b-hf",
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torch_dtype=torch.bfloat16,
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device_map="auto",
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quantization_config=quantization_config
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)
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tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-13b-hf")
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input_ids = tokenizer("Plants create energy through a process known as", return_tensors="pt").to("cuda")
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output = model.generate(**input_ids, cache_implementation="static")
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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Use the [AttentionMaskVisualizer](https://github.com/huggingface/transformers/blob/beb9b5b02246b9b7ee81ddf938f93f44cfeaad19/src/transformers/utils/attention_visualizer.py#L139) to better understand what tokens the model can and cannot attend to.
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```py
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from transformers.utils.attention_visualizer import AttentionMaskVisualizer
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visualizer = AttentionMaskVisualizer("meta-llama/Llama-2-7b-hf")
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visualizer("Plants create energy through a process known as")
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```
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<div class="flex justify-center">
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/llama-2-attn-mask.png"/>
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</div>
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## Notes
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- Setting `config.pretraining_tp` to a value besides `1` activates a more accurate but slower computation of the linear layers. This matches the original logits better.
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- The original model uses `pad_id = -1` to indicate a padding token. The Transformers implementation requires adding a padding token and resizing the token embedding accordingly.
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```py
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tokenizer.add_special_tokens({"pad_token":"<pad>"})
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# update model config with padding token
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model.config.pad_token_id
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```
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- It is recommended to initialize the `embed_tokens` layer with the following code to ensure encoding the padding token outputs zeros.
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```py
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self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.config.padding_idx)
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```
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- The tokenizer is a byte-pair encoding model based on [SentencePiece](https://github.com/google/sentencepiece). During decoding, if the first token is the start of the word (for example, "Banana"), the tokenizer doesn't prepend the prefix space to the string.
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- Don't use the `torch_dtype` parameter in [`~AutoModel.from_pretrained`] if you're using FlashAttention-2 because it only supports fp16 or bf16. You should use [Automatic Mixed Precision](https://pytorch.org/tutorials/recipes/recipes/amp_recipe.html), set fp16 or bf16 to `True` if using [`Trainer`], or use [torch.autocast](https://pytorch.org/docs/stable/amp.html#torch.autocast).
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## LlamaConfig
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[[autodoc]] LlamaConfig
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## LlamaTokenizer
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[[autodoc]] LlamaTokenizer
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- build_inputs_with_special_tokens
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- get_special_tokens_mask
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- create_token_type_ids_from_sequences
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- save_vocabulary
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## LlamaTokenizerFast
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[[autodoc]] LlamaTokenizerFast
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- build_inputs_with_special_tokens
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- get_special_tokens_mask
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- create_token_type_ids_from_sequences
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- update_post_processor
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- save_vocabulary
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## LlamaModel
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[[autodoc]] LlamaModel
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- forward
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## LlamaForCausalLM
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[[autodoc]] LlamaForCausalLM
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- forward
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## LlamaForSequenceClassification
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[[autodoc]] LlamaForSequenceClassification
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- forward
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