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Model: TheBloke/law-chat-GGUF Source: Original Platform
2026-07-05 23:56:12 +08:00
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--- a/LICENSE.txt
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 Llama 2 is licensed under the LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.
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 ---
 base_model: AdaptLLM/law-chat
 datasets:
 - EleutherAI/pile
 - Open-Orca/OpenOrca
 - GAIR/lima
 - WizardLM/WizardLM_evol_instruct_V2_196k
 inference: false
 language:
 - en
 license: llama2
 metrics:
 - accuracy
 model_creator: AdaptLLM
 model_name: Law Chat
 model_type: llama
 pipeline_tag: text-generation
 prompt_template: '[INST] <<SYS>>
  {system_message}
  <</SYS>>
  {prompt} [/INST]
  '
 quantized_by: TheBloke
 tags:
 - legal
 ---
 <!-- markdownlint-disable MD041 -->
 <!-- header start -->
 <!-- 200823 -->
 <div style="width: auto; margin-left: auto; margin-right: auto">
 <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
 </div>
 <div style="display: flex; justify-content: space-between; width: 100%;">
    <div style="display: flex; flex-direction: column; align-items: flex-start;">
        <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
    </div>
    <div style="display: flex; flex-direction: column; align-items: flex-end;">
        <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
    </div>
 </div>
 <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
 <hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
 <!-- header end -->
 # Law Chat - GGUF
 - Model creator: [AdaptLLM](https://huggingface.co/AdaptLLM)
 - Original model: [Law Chat](https://huggingface.co/AdaptLLM/law-chat)
 <!-- description start -->
 ## Description
 This repo contains GGUF format model files for [AdaptLLM's Law Chat](https://huggingface.co/AdaptLLM/law-chat).
 These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
 <!-- description end -->
 <!-- README_GGUF.md-about-gguf start -->
 ### About GGUF
 GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
 Here is an incomplete list of clients and libraries that are known to support GGUF:
 * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
 * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
 * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
 * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
 * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
 * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
 * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
 * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
 * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
 * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
 <!-- README_GGUF.md-about-gguf end -->
 <!-- repositories-available start -->
 ## Repositories available
 * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/law-chat-AWQ)
 * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/law-chat-GPTQ)
 * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/law-chat-GGUF)
 * [AdaptLLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/AdaptLLM/law-chat)
 <!-- repositories-available end -->
 <!-- prompt-template start -->
 ## Prompt template: Llama-2-Chat
 ```
 [INST] <<SYS>>
 {system_message}
 <</SYS>>
 {prompt} [/INST]
 ```
 <!-- prompt-template end -->
 <!-- compatibility_gguf start -->
 ## Compatibility
 These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
 They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
 ## Explanation of quantisation methods
 <details>
  <summary>Click to see details</summary>
 The new methods available are:
 * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
 * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
 * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
 * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
 * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
 Refer to the Provided Files table below to see what files use which methods, and how.
 </details>
 <!-- compatibility_gguf end -->
 <!-- README_GGUF.md-provided-files start -->
 ## Provided files
 | Name | Quant method | Bits | Size | Max RAM required | Use case |
 | ---- | ---- | ---- | ---- | ---- | ----- |
 | [law-chat.Q2_K.gguf](https://huggingface.co/TheBloke/law-chat-GGUF/blob/main/law-chat.Q2_K.gguf) | Q2_K | 2 | 2.83 GB| 5.33 GB | smallest, significant quality loss - not recommended for most purposes |
 | [law-chat.Q3_K_S.gguf](https://huggingface.co/TheBloke/law-chat-GGUF/blob/main/law-chat.Q3_K_S.gguf) | Q3_K_S | 3 | 2.95 GB| 5.45 GB | very small, high quality loss |
 | [law-chat.Q3_K_M.gguf](https://huggingface.co/TheBloke/law-chat-GGUF/blob/main/law-chat.Q3_K_M.gguf) | Q3_K_M | 3 | 3.30 GB| 5.80 GB | very small, high quality loss |
 | [law-chat.Q3_K_L.gguf](https://huggingface.co/TheBloke/law-chat-GGUF/blob/main/law-chat.Q3_K_L.gguf) | Q3_K_L | 3 | 3.60 GB| 6.10 GB | small, substantial quality loss |
 | [law-chat.Q4_0.gguf](https://huggingface.co/TheBloke/law-chat-GGUF/blob/main/law-chat.Q4_0.gguf) | Q4_0 | 4 | 3.83 GB| 6.33 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
 | [law-chat.Q4_K_S.gguf](https://huggingface.co/TheBloke/law-chat-GGUF/blob/main/law-chat.Q4_K_S.gguf) | Q4_K_S | 4 | 3.86 GB| 6.36 GB | small, greater quality loss |
 | [law-chat.Q4_K_M.gguf](https://huggingface.co/TheBloke/law-chat-GGUF/blob/main/law-chat.Q4_K_M.gguf) | Q4_K_M | 4 | 4.08 GB| 6.58 GB | medium, balanced quality - recommended |
 | [law-chat.Q5_0.gguf](https://huggingface.co/TheBloke/law-chat-GGUF/blob/main/law-chat.Q5_0.gguf) | Q5_0 | 5 | 4.65 GB| 7.15 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
 | [law-chat.Q5_K_S.gguf](https://huggingface.co/TheBloke/law-chat-GGUF/blob/main/law-chat.Q5_K_S.gguf) | Q5_K_S | 5 | 4.65 GB| 7.15 GB | large, low quality loss - recommended |
 | [law-chat.Q5_K_M.gguf](https://huggingface.co/TheBloke/law-chat-GGUF/blob/main/law-chat.Q5_K_M.gguf) | Q5_K_M | 5 | 4.78 GB| 7.28 GB | large, very low quality loss - recommended |
 | [law-chat.Q6_K.gguf](https://huggingface.co/TheBloke/law-chat-GGUF/blob/main/law-chat.Q6_K.gguf) | Q6_K | 6 | 5.53 GB| 8.03 GB | very large, extremely low quality loss |
 | [law-chat.Q8_0.gguf](https://huggingface.co/TheBloke/law-chat-GGUF/blob/main/law-chat.Q8_0.gguf) | Q8_0 | 8 | 7.16 GB| 9.66 GB | very large, extremely low quality loss - not recommended |
 **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
 <!-- README_GGUF.md-provided-files end -->
 <!-- README_GGUF.md-how-to-download start -->
 ## How to download GGUF files
 **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
 The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
 * LM Studio
 * LoLLMS Web UI
 * Faraday.dev
 ### In `text-generation-webui`
 Under Download Model, you can enter the model repo: TheBloke/law-chat-GGUF and below it, a specific filename to download, such as: law-chat.Q4_K_M.gguf.
 Then click Download.
 ### On the command line, including multiple files at once
 I recommend using the `huggingface-hub` Python library:
 ```shell
 pip3 install huggingface-hub
 ```
 Then you can download any individual model file to the current directory, at high speed, with a command like this:
 ```shell
 huggingface-cli download TheBloke/law-chat-GGUF law-chat.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
 ```
 <details>
  <summary>More advanced huggingface-cli download usage (click to read)</summary>
 You can also download multiple files at once with a pattern:
 ```shell
 huggingface-cli download TheBloke/law-chat-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
 ```
 For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
 To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
 ```shell
 pip3 install hf_transfer
 ```
 And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
 ```shell
 HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/law-chat-GGUF law-chat.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
 ```
 Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
 </details>
 <!-- README_GGUF.md-how-to-download end -->
 <!-- README_GGUF.md-how-to-run start -->
 ## Example `llama.cpp` command
 Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
 ```shell
 ./main -ngl 35 -m law-chat.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "[INST] <<SYS>>\n{system_message}\n<</SYS>>\n{prompt} [/INST]"
 ```
 Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
 Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
 If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
 For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
 ## How to run in `text-generation-webui`
 Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
 ## How to run from Python code
 You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
 ### How to load this model in Python code, using llama-cpp-python
 For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
 #### First install the package
 Run one of the following commands, according to your system:
 ```shell
 # Base ctransformers with no GPU acceleration
 pip install llama-cpp-python
 # With NVidia CUDA acceleration
 CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
 # Or with OpenBLAS acceleration
 CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
 # Or with CLBLast acceleration
 CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
 # Or with AMD ROCm GPU acceleration (Linux only)
 CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
 # Or with Metal GPU acceleration for macOS systems only
 CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
 # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
 $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
 pip install llama-cpp-python
 ```
 #### Simple llama-cpp-python example code
 ```python
 from llama_cpp import Llama
 # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
 llm = Llama(
  model_path="./law-chat.Q4_K_M.gguf",  # Download the model file first
  n_ctx=4096,  # The max sequence length to use - note that longer sequence lengths require much more resources
  n_threads=8,            # The number of CPU threads to use, tailor to your system and the resulting performance
  n_gpu_layers=35         # The number of layers to offload to GPU, if you have GPU acceleration available
 )
 # Simple inference example
 output = llm(
  "[INST] <<SYS>>\n{system_message}\n<</SYS>>\n{prompt} [/INST]", # Prompt
  max_tokens=512,  # Generate up to 512 tokens
  stop=["</s>"],   # Example stop token - not necessarily correct for this specific model! Please check before using.
  echo=True        # Whether to echo the prompt
 )
 # Chat Completion API
 llm = Llama(model_path="./law-chat.Q4_K_M.gguf", chat_format="llama-2")  # Set chat_format according to the model you are using
 llm.create_chat_completion(
    messages = [
        {"role": "system", "content": "You are a story writing assistant."},
        {
            "role": "user",
            "content": "Write a story about llamas."
        }
    ]
 )
 ```
 ## How to use with LangChain
 Here are guides on using llama-cpp-python and ctransformers with LangChain:
 * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
 * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
 <!-- README_GGUF.md-how-to-run end -->
 <!-- footer start -->
 <!-- 200823 -->
 ## Discord
 For further support, and discussions on these models and AI in general, join us at:
 [TheBloke AI's Discord server](https://discord.gg/theblokeai)
 ## Thanks, and how to contribute
 Thanks to the [chirper.ai](https://chirper.ai) team!
 Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
 I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
 If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
 Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
 * Patreon: https://patreon.com/TheBlokeAI
 * Ko-Fi: https://ko-fi.com/TheBlokeAI
 **Special thanks to**: Aemon Algiz.
 **Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
 Thank you to all my generous patrons and donaters!
 And thank you again to a16z for their generous grant.
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 <!-- original-model-card start -->
 # Original model card: AdaptLLM's Law Chat
 # Adapt (Large) Language Models to Domains
 This repo contains the domain-specific chat model developed from **LLaMA-2-Chat-7B**, using the method in our paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530).
 We explore **continued pre-training on domain-specific corpora** for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to **transform large-scale pre-training corpora into reading comprehension texts**, consistently improving prompting performance across tasks in biomedicine, finance, and law domains. **Our 7B model competes with much larger domain-specific models like BloombergGPT-50B**.
 ### 🤗 We are currently working hard on developing models across different domains, scales and architectures! Please stay tuned! 🤗
 **************************** **Updates** ****************************
 * 12/19: Released our [13B base models](https://huggingface.co/AdaptLLM/law-LLM-13B) developed from LLaMA-1-13B.
 * 12/8: Released our [chat models](https://huggingface.co/AdaptLLM/law-chat) developed from LLaMA-2-Chat-7B.
 * 9/18: Released our [paper](https://huggingface.co/papers/2309.09530), [code](https://github.com/microsoft/LMOps), [data](https://huggingface.co/datasets/AdaptLLM/law-tasks), and [base models](https://huggingface.co/AdaptLLM/law-LLM) developed from LLaMA-1-7B.
 ## Domain-Specific LLaMA-1
 ### LLaMA-1-7B
 In our paper, we develop three domain-specific models from LLaMA-1-7B, which are also available in Huggingface: [Biomedicine-LLM](https://huggingface.co/AdaptLLM/medicine-LLM), [Finance-LLM](https://huggingface.co/AdaptLLM/finance-LLM) and [Law-LLM](https://huggingface.co/AdaptLLM/law-LLM), the performances of our AdaptLLM compared to other domain-specific LLMs are:
 <p align='center'>
    <img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/6efPwitFgy-pLTzvccdcP.png" width="700">
 </p>
 ### LLaMA-1-13B
 Moreover, we scale up our base model to LLaMA-1-13B to see if **our method is similarly effective for larger-scale models**, and the results are consistently positive too: [Biomedicine-LLM-13B](https://huggingface.co/AdaptLLM/medicine-LLM-13B), [Finance-LLM-13B](https://huggingface.co/AdaptLLM/finance-LLM-13B) and [Law-LLM-13B](https://huggingface.co/AdaptLLM/law-LLM-13B).
 ## Domain-Specific LLaMA-2-Chat
 Our method is also effective for aligned models! LLaMA-2-Chat requires a [specific data format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2), and our **reading comprehension can perfectly fit the data format** by transforming the reading comprehension into a multi-turn conversation. We have also open-sourced chat models in different domains: [Biomedicine-Chat](https://huggingface.co/AdaptLLM/medicine-chat), [Finance-Chat](https://huggingface.co/AdaptLLM/finance-chat) and [Law-Chat](https://huggingface.co/AdaptLLM/law-chat)
 For example, to chat with the law-chat model:
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 model = AutoModelForCausalLM.from_pretrained("AdaptLLM/law-chat")
 tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/law-chat")
 # Put your input here:
 user_input = '''Question: Which of the following is false about ex post facto laws?
 Options:
 - They make criminal an act that was innocent when committed.
 - They prescribe greater punishment for an act than was prescribed when it was done.
 - They increase the evidence required to convict a person than when the act was done.
 - They alter criminal offenses or punishment in a substantially prejudicial manner for the purpose of punishing a person for some past activity.
 Please provide your choice first and then provide explanations if possible.'''
 # Apply the prompt template and system prompt of LLaMA-2-Chat demo for chat models (NOTE: NO prompt template is required for base models!)
 our_system_prompt = "\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n" # Please do NOT change this
 prompt = f"<s>[INST] <<SYS>>{our_system_prompt}<</SYS>>\n\n{user_input} [/INST]"
 # # NOTE:
 # # If you want to apply your own system prompt, please integrate it into the instruction part following our system prompt like this:
 # your_system_prompt = "Please, answer this question faithfully."
 # prompt = f"<s>[INST] <<SYS>>{our_system_prompt}<</SYS>>\n\n{your_system_prompt}\n{user_input} [/INST]"
 inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device)
 outputs = model.generate(input_ids=inputs, max_length=4096)[0]
 answer_start = int(inputs.shape[-1])
 pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True)
 print(f'### User Input:\n{user_input}\n\n### Assistant Output:\n{pred}')
 ```
 ## Domain-Specific Tasks
 To easily reproduce our results, we have uploaded the filled-in zero/few-shot input instructions and output completions of each domain-specific task: [biomedicine-tasks](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), [finance-tasks](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [law-tasks](https://huggingface.co/datasets/AdaptLLM/law-tasks).
 **Note:** those filled-in instructions are specifically tailored for models before alignment and do NOT fit for the specific data format required for chat models.
 ## Citation
 If you find our work helpful, please cite us:
 ```bibtex
@article{adaptllm,
  title        = {Adapting Large Language Models via Reading Comprehension},
  author       = {Daixuan Cheng and Shaohan Huang and Furu Wei},
  journal      = {CoRR},
  volume       = {abs/2309.09530},
  year         = {2023}
 }
 ```
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--- a/USE_POLICY.md
+++ b/USE_POLICY.md
@@ -0,0 +1,50 @@
 # Llama 2 Acceptable Use Policy
 Meta is committed to promoting safe and fair use of its tools and features, including Llama 2. If you access or use Llama 2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [ai.meta.com/llama/use-policy](http://ai.meta.com/llama/use-policy).
 ## Prohibited Uses
 We want everyone to use Llama 2 safely and responsibly. You agree you will not use, or allow others to use, Llama 2 to: 
 1. Violate the law or others’ rights, including to:
    1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 
        1. Violence or terrorism 
        2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
        3. Human trafficking, exploitation, and sexual violence
        4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
        5. Sexual solicitation
        6. Any other criminal activity
    2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
    3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
    4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 
    5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
    6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials
    7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 
 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 2 related to the following:
    1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
    2. Guns and illegal weapons (including weapon development)
    3. Illegal drugs and regulated/controlled substances
    4. Operation of critical infrastructure, transportation technologies, or heavy machinery
    5. Self-harm or harm to others, including suicide, cutting, and eating disorders
    6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
 3. Intentionally deceive or mislead others, including use of Llama 2 related to the following:
    1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
    2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
    3. Generating, promoting, or further distributing spam
    4. Impersonating another individual without consent, authorization, or legal right
    5. Representing that the use of Llama 2 or outputs are human-generated
    6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 
 4. Fail to appropriately disclose to end users any known dangers of your AI system 
 Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means:
 * Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
 * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
 * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
 * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: [LlamaUseReport@meta.com](mailto:LlamaUseReport@meta.com)
--- a/config.json
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    "model_type": "llama"
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							`Llama 2 is licensed under the LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.`
		`@@ -0,0 +1 @@`
							`{"framework": "pytorch", "task": "text-generation", "allow_remote": true}`