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* 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>
154 lines
5.1 KiB
Markdown
154 lines
5.1 KiB
Markdown
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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<div style="float: right;">
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<div class="flex flex-wrap space-x-1">
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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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</div>
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# Falcon
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[Falcon](https://huggingface.co/papers/2311.16867) is a family of large language models, available in 7B, 40B, and 180B parameters, as pretrained and instruction tuned variants. This model focuses on scaling pretraining over three categories, performance, data, and hardware. Falcon uses multigroup attention to significantly reduce inference memory requirements and rotary positional embeddings (RoPE). These models are pretrained on [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), a high-quality and deduplicated 5T token dataset.
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You can find all the original Falcon checkpoints under the [Falcon](https://huggingface.co/collections/tiiuae/falcon-64fb432660017eeec9837b5a) collection.
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> [!TIP]
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> Click on the Falcon models in the right sidebar for more examples of how to apply Falcon to different language tasks.
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The example below demonstrates how to generate text with [`Pipeline`], [`AutoModel`], and 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="tiiuae/falcon-7b-instruct",
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torch_dtype=torch.bfloat16,
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device=0
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)
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pipeline(
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"Write a short poem about coding",
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max_length=100,
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do_sample=True,
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temperature=0.7
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)
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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 AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-7b-instruct")
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model = AutoModelForCausalLM.from_pretrained(
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"tiiuae/falcon-7b-instruct",
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torch_dtype=torch.bfloat16,
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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("Write a short poem about coding", return_tensors="pt").to("cuda")
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output = model.generate(**input_ids)
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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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# pip install -U flash-attn --no-build-isolation
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transformers chat tiiuae/falcon-7b-instruct --torch_dtype auto --attn_implementation flash_attention_2 --device 0
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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 [bitsandbytes](../quantization/bitsandbytes) to only quantize the weights to 4-bits.
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```python
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import torch
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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_compute_dtype=torch.bfloat16,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_use_double_quant=True,
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)
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tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-7b")
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model = AutoModelForCausalLM.from_pretrained(
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"tiiuae/falcon-7b",
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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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inputs = tokenizer("In quantum physics, entanglement means", return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=100)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Notes
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- If you're upgrading from an older custom code checkpoint, remember to convert it to the official Transformers format for better stability and performance using the conversion script located in the [Falcon model directory](https://github.com/huggingface/transformers/tree/main/src/transformers/models/falcon).
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```bash
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python convert_custom_code_checkpoint.py --checkpoint_dir my_model
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```
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## FalconConfig
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[[autodoc]] FalconConfig
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- all
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## FalconModel
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[[autodoc]] FalconModel
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- forward
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## FalconForCausalLM
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[[autodoc]] FalconForCausalLM
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- forward
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## FalconForSequenceClassification
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[[autodoc]] FalconForSequenceClassification
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- forward
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## FalconForTokenClassification
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[[autodoc]] FalconForTokenClassification
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- forward
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## FalconForQuestionAnswering
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[[autodoc]] FalconForQuestionAnswering
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- forward
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