--- base_model: jondurbin/bagel-34b-v0.2 datasets: - ai2_arc - unalignment/spicy-3.1 - codeparrot/apps - facebook/belebele - boolq - jondurbin/cinematika-v0.1 - drop - lmsys/lmsys-chat-1m - TIGER-Lab/MathInstruct - cais/mmlu - Muennighoff/natural-instructions - openbookqa - piqa - Vezora/Tested-22k-Python-Alpaca - cakiki/rosetta-code - Open-Orca/SlimOrca - spider - squad_v2 - migtissera/Synthia-v1.3 - datasets/winogrande - nvidia/HelpSteer - Intel/orca_dpo_pairs - unalignment/toxic-dpo-v0.1 - jondurbin/truthy-dpo-v0.1 - allenai/ultrafeedback_binarized_cleaned - Squish42/bluemoon-fandom-1-1-rp-cleaned - LDJnr/Capybara - JULIELab/EmoBank - kingbri/PIPPA-shareGPT inference: false license: other license_link: https://huggingface.co./01-ai/Yi-34B-200K/blob/main/LICENSE license_name: yi-license model_creator: Jon Durbin model_name: Bagel 34B v0.2 model_type: yi prompt_template: 'Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {system_message} {prompt} ### Response: ' quantized_by: TheBloke ---
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# Bagel 34B v0.2 - AWQ - Model creator: [Jon Durbin](https://huggingface.co./jondurbin) - Original model: [Bagel 34B v0.2](https://huggingface.co./jondurbin/bagel-34b-v0.2) ## Description This repo contains AWQ model files for [Jon Durbin's Bagel 34B v0.2](https://huggingface.co./jondurbin/bagel-34b-v0.2). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead. It is supported by: - [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ - [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) - [Transformers](https://huggingface.co./docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co./TheBloke/bagel-34b-v0.2-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co./TheBloke/bagel-34b-v0.2-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co./TheBloke/bagel-34b-v0.2-GGUF) * [Jon Durbin's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co./jondurbin/bagel-34b-v0.2) ## Prompt template: Bagel-Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {system_message} {prompt} ### Response: ``` ## Provided files, and AWQ parameters I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered. Models are released as sharded safetensors files. | Branch | Bits | GS | AWQ Dataset | Seq Len | Size | | ------ | ---- | -- | ----------- | ------- | ---- | | [main](https://huggingface.co./TheBloke/bagel-34b-v0.2-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co./datasets/VMware/open-instruct/viewer/) | 8192 | 19.23 GB ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/bagel-34b-v0.2-AWQ`. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `bagel-34b-v0.2-AWQ` 7. Select **Loader: AutoAWQ**. 8. Click Load, and the model will load and is now ready for use. 9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. 10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started! ## Multi-user inference server: vLLM Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/). - Please ensure you are using vLLM version 0.2 or later. - When using vLLM as a server, pass the `--quantization awq` parameter. For example: ```shell python3 -m vllm.entrypoints.api_server --model TheBloke/bagel-34b-v0.2-AWQ --quantization awq --dtype auto ``` - When using vLLM from Python code, again set `quantization=awq`. For example: ```python from vllm import LLM, SamplingParams prompts = [ "Tell me about AI", "Write a story about llamas", "What is 291 - 150?", "How much wood would a woodchuck chuck if a woodchuck could chuck wood?", ] prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {system_message} {prompt} ### Response: ''' prompts = [prompt_template.format(prompt=prompt) for prompt in prompts] sampling_params = SamplingParams(temperature=0.8, top_p=0.95) llm = LLM(model="TheBloke/bagel-34b-v0.2-AWQ", quantization="awq", dtype="auto") outputs = llm.generate(prompts, sampling_params) # Print the outputs. for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") ``` ## Multi-user inference server: Hugging Face Text Generation Inference (TGI) Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/bagel-34b-v0.2-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {system_message} {prompt} ### Response: ''' client = InferenceClient(endpoint_url) response = client.text_generation(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1) print(f"Model output: ", response) ``` ## Inference from Python code using Transformers ### Install the necessary packages - Requires: [Transformers](https://huggingface.co./docs/transformers) 4.35.0 or later. - Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later. ```shell pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0" ``` Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0. If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command: ```shell pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl ``` If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y autoawq git clone https://github.com/casper-hansen/AutoAWQ cd AutoAWQ pip3 install . ``` ### Transformers example code (requires Transformers 4.35.0 and later) ```python from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer model_name_or_path = "TheBloke/bagel-34b-v0.2-AWQ" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) model = AutoModelForCausalLM.from_pretrained( model_name_or_path, low_cpu_mem_usage=True, device_map="cuda:0" ) # Using the text streamer to stream output one token at a time streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) prompt = "Tell me about AI" prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {system_message} {prompt} ### Response: ''' # Convert prompt to tokens tokens = tokenizer( prompt_template, return_tensors='pt' ).input_ids.cuda() generation_params = { "do_sample": True, "temperature": 0.7, "top_p": 0.95, "top_k": 40, "max_new_tokens": 512, "repetition_penalty": 1.1 } # Generate streamed output, visible one token at a time generation_output = model.generate( tokens, streamer=streamer, **generation_params ) # Generation without a streamer, which will include the prompt in the output generation_output = model.generate( tokens, **generation_params ) # Get the tokens from the output, decode them, print them token_output = generation_output[0] text_output = tokenizer.decode(token_output) print("model.generate output: ", text_output) # Inference is also possible via Transformers' pipeline from transformers import pipeline pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, **generation_params ) pipe_output = pipe(prompt_template)[0]['generated_text'] print("pipeline output: ", pipe_output) ``` ## Compatibility The files provided are tested to work with: - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`. - [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later. - [Transformers](https://huggingface.co./docs/transformers) version 4.35.0 and later. - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later. ## 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. # Original model card: Jon Durbin's Bagel 34B v0.2 # A bagel, with everything (except DPO) ![bagel](bagel.png) ## Overview An experimental fine-tune of yi-34b-200k using [bagel](https://github.com/jondurbin/bagel) This is the model after the SFT phase, before DPO has been applied. You'll likely want to use the DPO'd version, rather than this one, but since I had it, I uploaded it. ### Data sources *Yes, you will see benchmark names in the list, but this only uses the train splits, and a decontamination by cosine similarity is performed at the end as a sanity check* - [ai2_arc](https://huggingface.co./datasets/ai2_arc) - Abstraction and reasoning dataset, useful in measuring "intelligence" to a certain extent. - [airoboros](https://huggingface.co./datasets/unalignment/spicy-3.1) - Variety of categories of synthetic instructions generated by gpt-4. - [apps](https://huggingface.co./datasets/codeparrot/apps) - Python coding dataset with 10k problems. - [belebele](https://huggingface.co./datasets/facebook/belebele) - Multi-lingual reading comprehension dataset. - [bluemoon](https://huggingface.co./datasets/Squish42/bluemoon-fandom-1-1-rp-cleaned) - Roleplay data scraped from Bluemoon, then cleaned and formatted as ShareGPT. - [boolq](https://huggingface.co./datasets/boolq) - Corpus of yes/no questions (which can be surprisingly difficult for AI to answer apparently?) - [capybara](https://huggingface.co./datasets/LDJnr/Capybara) - Multi-turn dataset used to create the capybara models. - [cinematika](https://huggingface.co./datasets/jondurbin/cinematika-v0.1) (instruction and plain text) - RP-style data synthesized from movie scripts so the model isn't quite as boring as it otherwise would be. - [drop](https://huggingface.co./datasets/drop) - More reading comprehension. - [emobank](https://github.com/JULIELab/EmoBank) - Emotion annotations using the Valence-Arousal-Domninance scheme. - [gutenberg](https://www.gutenberg.org/) (plain text) - Books/plain text, again to make the model less boring, only a handful of examples supported by [chapterize](https://github.com/JonathanReeve/chapterize) - [lmsys_chat_1m](https://huggingface.co./datasets/lmsys/lmsys-chat-1m) (only gpt-4 items, also used for DPO) - Chats collected by the lmsys chat arena, containing a wide variety of chats with various models. - [mathinstruct](https://huggingface.co./datasets/TIGER-Lab/MathInstruct) - Composite dataset with a variety of math-related tasks and problem/question formats. - [mmlu](https://huggingface.co./datasets/cais/mmlu) - Massive Multitask Language Understanding - a wide variety of questions about various subject matters. - [natural_instructions](https://huggingface.co./datasets/Muennighoff/natural-instructions) - Millions of instructions from 1600+ task categories (sampled down substantially, stratified by task type) - [openbookqa](https://huggingface.co./datasets/openbookqa) - Question answering dataset. - [pippa](https://huggingface.co./datasets/kingbri/PIPPA-shareGPT) - Deduped version of [PIPPA](https://huggingface.co./datasets/PygmalionAI/PIPPA) in ShareGPT format. - [piqa](https://huggingface.co./datasets/piqa) - Phyiscal interaction question answering. - [python_alpaca](https://huggingface.co./datasets/Vezora/Tested-22k-Python-Alpaca) - Python instruction response pairs, validated as functional. - [rosetta_code](https://huggingface.co./datasets/cakiki/rosetta-code) - Code problems and solutions in a variety of programming languages taken from rosettacode.org. - [slimorca](https://huggingface.co./datasets/Open-Orca/SlimOrca) - Collection of ~500k gpt-4 verified chats from OpenOrca. - [spider](https://huggingface.co./datasets/spider) - SQL-targeted dataset. - [squad_v2](https://huggingface.co./datasets/squad_v2) - Contextual question answering (RAG). - [synthia](https://huggingface.co./datasets/migtissera/Synthia-v1.3) - GPT-4 generated data using advanced prompting from Migel Tissera. - [winogrande](https://huggingface.co./datasets/winogrande) - Fill in the blank style prompts. Only the train splits were used (if a split was provided), and an additional pass of decontamination is performed using approximate nearest neighbor search (via faiss). ## Prompt formatting In sticking with the theme of the bagel, I didn't want to use a single prompt format, so I used 4 - vicuna, llama-2, alpaca, and chat-ml (sorta). I also didn't want to randomly select a single prompt format for each item (hoping each instruction would generalize more when used in a variety of prompt formats), so each instruction is actually converted into every prompt format. This means each epoch of our fine-tune is really basically 4 epochs. So, for the fine-tunes, I would recommend only doing 1 epoch (or 0.75 epochs). I am testing with a single epoch using a relatively low learning rate. ### Alpaca (sort of) ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {system prompt, if provided} {instruction} ### Response: ``` The main difference here is that because of the dataset formatting and variety of data sources, it would have been much to tedious to add an `### Input:` block, so the inputs are just in the instruction section. ### Vicuna ``` {system prompt, if provided, randomly defaulting to "A chat between a user and an unbiased, uncensored assistant."} USER: {instruction} ASSISTANT: ``` ### ChatML (sort of) I don't really understand the point of having special tokens for `<|im_start|>` and `<|im_end|>`, because in practice they just act as BOS and EOS tokens (but, please correct me if I'm wrong). So, instead of: ```text {bos}<|im_start|>{role} {text} <|im_end|>{eos} ``` I just changed it to: ```text {bos}{role} {text} {eos} ``` If you *really* want to use `<|im_start|>` and `<|im_end|>`, just update your `tokenizer_config.json` to use `<|im_start|>` instead of `` and `<|im_end|>` instead of `` and when tokenizing. And if you still don't like what I've done to this chat-ml-ish format, feel free to cry into your pillow or fork the code and do a new fine-tune. ### Llama-2 chat ``` [INST] <> {system} <> {instruction} [/INST] ```