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Model: togethercomputer/LLaMA-2-7B-32K Source: Original Platform
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README.md
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---
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license: llama2
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datasets:
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- togethercomputer/RedPajama-Data-1T
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- togethercomputer/RedPajama-Data-Instruct
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- EleutherAI/pile
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- togethercomputer/Long-Data-Collections
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language:
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- en
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library_name: transformers
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---
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# LLaMA-2-7B-32K
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## Model Description
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LLaMA-2-7B-32K is an open-source, long context language model developed by Together, fine-tuned from Meta's original Llama-2 7B model.
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This model represents our efforts to contribute to the rapid progress of the open-source ecosystem for large language models.
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The model has been extended to a context length of 32K with position interpolation,
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allowing applications on multi-document QA, long text summarization, etc.
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## What's new?
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This model introduces several improvements and new features:
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1. **Extended Context:** The model has been trained to handle context lengths up to 32K, which is a significant improvement over the previous versions.
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2. **Pre-training and Instruction Tuning:** We have shared our data recipe, which consists of a mixture of pre-training and instruction tuning data.
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3. **Fine-tuning Examples:** We provide examples of how to fine-tune the model for specific applications, including book summarization and long context question and answering.
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4. **Software Support:** We have updated both the inference and training stack to allow efficient inference and fine-tuning for 32K context.
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## Model Architecture
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The model follows the architecture of Llama-2-7B and extends it to handle a longer context. It leverages the recently released FlashAttention-2 and a range of other optimizations to improve the speed and efficiency of inference and training.
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## Training and Fine-tuning
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The model has been trained using a mixture of pre-training and instruction tuning data.
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- In the first training phase of continued pre-training, our data mixture contains 25% RedPajama Book, 25% RedPajama ArXiv (including abstracts), 25% other data from RedPajama, and 25% from the UL2 Oscar Data, which is a part of OIG (Open-Instruction-Generalist), asking the model to fill in missing chunks, or complete the text.
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To enhance the long-context ability, we exclude data shorter than 2K word. The inclusion of UL2 Oscar Data is effective in compelling the model to read and utilize long-range context.
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- We then fine-tune the model to focus on its few shot capacity under long context, including 20% Natural Instructions (NI), 20% Public Pool of Prompts (P3), 20% the Pile. We decontaminated all data against HELM core scenarios . We teach the model to leverage the in-context examples by packing examples into one 32K-token sequence. To maintain the knowledge learned from the first piece of data, we incorporate 20% RedPajama-Data Book and 20% RedPajama-Data ArXiv.
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Next, we provide examples of how to fine-tune the model for specific applications.
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The example datasets are placed in [togethercomputer/Long-Data-Collections](https://huggingface.co/datasets/togethercomputer/Long-Data-Collections)
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You can use the [OpenChatKit](https://github.com/togethercomputer/OpenChatKit) to fine-tune your own 32K model over LLaMA-2-7B-32K.
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Please refer to [OpenChatKit](https://github.com/togethercomputer/OpenChatKit) for step-by-step illustrations.
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1. Long Context QA.
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We take as an example the multi-document question answering task from the paper “Lost in the Middle: How Language Models Use Long Contexts”. The input for the model consists of (i) a question that requires an answer and (ii) k documents, which are passages extracted from Wikipedia. Notably, only one of these documents contains the answer to the question, while the remaining k − 1 documents, termed as "distractor" documents, do not. To successfully perform this task, the model must identify and utilize the document containing the answer from its input context.
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With OCK, simply run the following command to fine-tune:
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```
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bash training/finetune_llama-2-7b-32k-mqa.sh
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```
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2. Summarization.
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Another example is BookSum, a unique dataset designed to address the challenges of long-form narrative summarization. This dataset features source documents from the literature domain, including novels, plays, and stories, and offers human-written, highly abstractive summaries. We here focus on chapter-level data. BookSum poses a unique set of challenges, necessitating that the model comprehensively read through each chapter.
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With OCK, simply run the following command to fine-tune:
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```
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bash training/finetune_llama-2-7b-32k-booksum.sh
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```
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## Inference
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You can use the [Together API](https://together.ai/blog/api-announcement) to try out LLaMA-2-7B-32K for inference.
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The updated inference stack allows for efficient inference.
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To run the model locally, we strongly recommend to install Flash Attention V2, which is necessary to obtain the best performance:
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```
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# Please update the path of `CUDA_HOME`
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export CUDA_HOME=/usr/local/cuda-11.8
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pip install transformers==4.31.0
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pip install sentencepiece
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pip install ninja
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pip install flash-attn --no-build-isolation
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pip install git+https://github.com/HazyResearch/flash-attention.git#subdirectory=csrc/rotary
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```
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You can use this model directly from the Hugging Face Model Hub or fine-tune it on your own data using the OpenChatKit.
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("togethercomputer/LLaMA-2-7B-32K")
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model = AutoModelForCausalLM.from_pretrained("togethercomputer/LLaMA-2-7B-32K", trust_remote_code=True, torch_dtype=torch.float16)
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input_context = "Your text here"
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input_ids = tokenizer.encode(input_context, return_tensors="pt")
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output = model.generate(input_ids, max_length=128, temperature=0.7)
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output_text = tokenizer.decode(output[0], skip_special_tokens=True)
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print(output_text)
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```
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Alternatively, you can set `trust_remote_code=False` if you prefer not to use flash attention.
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## Limitations and Bias
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As with all language models, LLaMA-2-7B-32K may generate incorrect or biased content. It's important to keep this in mind when using the model.
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## Community
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Join us on [Together Discord](https://discord.gg/6ZVDU8tTD4)
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|
||||
}
|
||||
}
|
||||
6
special_tokens_map.json
Normal file
6
special_tokens_map.json
Normal file
@@ -0,0 +1,6 @@
|
||||
{
|
||||
"bos_token": "<s>",
|
||||
"eos_token": "</s>",
|
||||
"pad_token": "<unk>",
|
||||
"unk_token": "<unk>"
|
||||
}
|
||||
93363
tokenizer.json
Normal file
93363
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
|
||||
size 499723
|
||||
42
tokenizer_config.json
Normal file
42
tokenizer_config.json
Normal file
@@ -0,0 +1,42 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_eos_token": false,
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
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|
||||
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|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
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|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [],
|
||||
"bos_token": "<s>",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "</s>",
|
||||
"legacy": false,
|
||||
"model_max_length": 32768,
|
||||
"pad_token": "<unk>",
|
||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": true
|
||||
}
|
||||
Reference in New Issue
Block a user