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<!--Copyright 2024 The GLM & ZhipuAI team and The HuggingFace Team. All rights reserved. | ||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with | ||
the License. You may obtain a copy of the License at | ||
http://www.apache.org/licenses/LICENSE-2.0 | ||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on | ||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the | ||
specific language governing permissions and limitations under the License. | ||
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be | ||
rendered properly in your Markdown viewer. | ||
--> | ||
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# GLM | ||
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## Overview | ||
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The GLM Model was proposed | ||
in [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools](https://arxiv.org/html/2406.12793v1) | ||
by GLM Team, THUDM & ZhipuAI. | ||
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The abstract from the paper is the following: | ||
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*We introduce ChatGLM, an evolving family of large language models that we have been developing over time. This report | ||
primarily focuses on the GLM-4 language series, which includes GLM-4, GLM-4-Air, and GLM-4-9B. They represent our most | ||
capable models that are trained with all the insights and lessons gained from the preceding three generations of | ||
ChatGLM. To date, the GLM-4 models are pre-trained on ten trillions of tokens mostly in Chinese and English, along with | ||
a small set of corpus from 24 languages, and aligned primarily for Chinese and English usage. The high-quality alignment | ||
is achieved via a multi-stage post-training process, which involves supervised fine-tuning and learning from human | ||
feedback. Evaluations show that GLM-4 1) closely rivals or outperforms GPT-4 in terms of general metrics such as MMLU, | ||
GSM8K, MATH, BBH, GPQA, and HumanEval, 2) gets close to GPT-4-Turbo in instruction following as measured by IFEval, 3) | ||
matches GPT-4 Turbo (128K) and Claude 3 for long context tasks, and 4) outperforms GPT-4 in Chinese alignments as | ||
measured by AlignBench. The GLM-4 All Tools model is further aligned to understand user intent and autonomously decide | ||
when and which tool(s) to use—including web browser, Python interpreter, text-to-image model, and user-defined | ||
functions—to effectively complete complex tasks. In practical applications, it matches and even surpasses GPT-4 All | ||
Tools in tasks like accessing online information via web browsing and solving math problems using Python interpreter. | ||
Over the course, we have open-sourced a series of models, including ChatGLM-6B (three generations), GLM-4-9B (128K, 1M), | ||
GLM-4V-9B, WebGLM, and CodeGeeX, attracting over 10 million downloads on Hugging face in the year 2023 alone.* | ||
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Tips: | ||
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- This model was contributed by [THUDM](https://huggingface.co/THUDM). The most recent code can be | ||
found [here](https://github.com/thudm/GLM-4). | ||
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## Usage tips | ||
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`GLM-4` can be found on the [Huggingface Hub](https://huggingface.co/collections/THUDM/glm-4-665fcf188c414b03c2f7e3b7) | ||
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In the following, we demonstrate how to use `glm-4-9b-chat` for the inference. Note that we have used the ChatML format for dialog, in this demo we show how to leverage `apply_chat_template` for this purpose. | ||
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```python | ||
>>> from transformers import AutoModelForCausalLM, AutoTokenizer | ||
>>> device = "cuda" # the device to load the model onto | ||
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>>> model = AutoModelForCausalLM.from_pretrained("THUDM/glm-4-9b-chat", device_map="auto") | ||
>>> tokenizer = AutoTokenizer.from_pretrained("THUDM/glm-4-9b-chat") | ||
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>>> prompt = "Give me a short introduction to large language model." | ||
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>>> messages = [{"role": "user", "content": prompt}] | ||
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>>> text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) | ||
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>>> model_inputs = tokenizer([text], return_tensors="pt").to(device) | ||
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>>> generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=512, do_sample=True) | ||
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>>> generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)] | ||
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>>> response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] | ||
``` | ||
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## GlmConfig | ||
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[[autodoc]] GlmConfig | ||
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## GlmModel | ||
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[[autodoc]] GlmModel | ||
- forward | ||
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## GlmForCausalLM | ||
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[[autodoc]] GlmForCausalLM | ||
- forward | ||
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## GlmForSequenceClassification | ||
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[[autodoc]] GlmForSequenceClassification | ||
- forward | ||
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## GlmForTokenClassification | ||
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[[autodoc]] GlmForTokenClassification | ||
- forward |
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gemma, | ||
gemma2, | ||
git, | ||
glm, | ||
glpn, | ||
gpt2, | ||
gpt_bigcode, | ||
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