--- datasets: - tiiuae/falcon-refinedweb language: - en inference: false ---
TheBlokeAI

Chat & support: my new Discord server

Want to contribute? TheBloke's Patreon page

# Falcon-40B-Instruct GPTQ This repo contains an experimantal GPTQ 4bit model for [Falcon-40B-Instruct](https://huggingface.co/tiiuae/falcon-40b-instruct). It is the result of quantising to 4bit using [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ). ## EXPERIMENTAL Please note this is an experimental GPTQ model. Support for it is currently quite limited. It is also expected to be **VERY SLOW**. This is unavoidable at the moment, but is being looked at. To use it you will require: 1. AutoGPTQ, from the latest `main` branch and compiled with `pip install .` 2. `pip install einops` You can then use it immediately from Python code - see example code below - or from text-generation-webui. ## AutoGPTQ To install AutoGPTQ please follow these instructions: ``` git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ pip install . ``` These steps will require that you have the [Nvidia CUDA toolkit](https://developer.nvidia.com/cuda-12-0-1-download-archive) installed. ## text-generation-webui There is also provisional AutoGPTQ support in text-generation-webui. This requires text-generation-webui as of commit 204731952ae59d79ea3805a425c73dd171d943c3. So please first update text-genration-webui to the latest version. ## How to download and use this model in text-generation-webui 1. Launch text-generation-webui with the following command-line arguments: `--autogptq --trust-remote-code` 2. Click the **Model tab**. 3. Under **Download custom model or LoRA**, enter `TheBloke/falcon-40B-instruct-GPTQ`. 4. Click **Download**. 5. Wait until it says it's finished downloading. 6. Click the **Refresh** icon next to **Model** in the top left. 7. In the **Model drop-down**: choose the model you just downloaded, `falcon-40B-instruct-GPTQ`. 8. Once it says it's loaded, click the **Text Generation tab** and enter a prompt! ## About `trust_remote_code` Please be aware that this command line argument causes Python code provided by Falcon to be executed on your machine. This code is required at the moment because Falcon is too new to be supported by Hugging Face transformers. At some point in the future transformers will support the model natively, and then `trust_remote_code` will no longer be needed. In this repo you can see two `.py` files - these are the files that get executed. They are copied from the base repo at [Falcon-7B-Instruct](https://huggingface.co/tiiuae/falcon-7b-instruct). ## Simple Python example code To run this code you need to install AutoGPTQ from source: ``` git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ pip install . # This step requires CUDA toolkit installed ``` And install einops: ``` pip install einops ``` You can then run this example code: ```python import torch from transformers import AutoTokenizer from auto_gptq import AutoGPTQForCausalLM # Download the model from HF and store it locally, then reference its location here: quantized_model_dir = "/path/to/falcon7b-instruct-gptq" from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(quantized_model_dir, use_fast=False) model = AutoGPTQForCausalLM.from_quantized(quantized_model_dir, device="cuda:0", use_triton=False, use_safetensors=True, torch_dtype=torch.float32, trust_remote_code=True) prompt = "Write a story about llamas" prompt_template = f"### Instruction: {prompt}\n### Response:" tokens = tokenizer(prompt_template, return_tensors="pt").to("cuda:0").input_ids output = model.generate(input_ids=tokens, max_new_tokens=100, do_sample=True, temperature=0.8) print(tokenizer.decode(output[0])) ``` ## Provided files **gptq_model-4bit.safetensors** This will work with AutoGPTQ as of commit `3cb1bf5` (`3cb1bf5a6d43a06dc34c6442287965d1838303d3`) It was created with no groupsize to reduce VRAM requirements as much as possible, with `desc_act` (act-order) to increase inference quality. * `gptq_model-4bit.safetensors` * Works only with latest AutoGPTQ CUDA, compiled from source as of commit `3cb1bf5` * At this time it does not work with AutoGPTQ Triton, but support will hopefully be added in time. * Works with text-generation-webui using `--autogptq --trust_remote_code` * At this time it does NOT work with one-click-installers * Does not work with any version of GPTQ-for-LLaMa * Parameters: Groupsize = 64. No act-order. ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/UBgz4VXf) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! 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'd be most gratefully received and will help me to keep providing models, and work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Patreon special mentions**: Aemon Algiz; Talal Aujan; Jonathan Leane; Illia Dulskyi; Khalefa Al-Ahmad; senxiiz. Thank you all, and to all my other generous patrons and donaters. # ✨ Original model card: Falcon-40B-Instruct # ✨ Falcon-40B-Instruct **Falcon-40B-Instruct is a 40B parameters causal decoder-only model built by [TII](https://www.tii.ae) based on [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) and finetuned on a mixture of [Baize](https://github.com/project-baize/baize-chatbot). It is made available under the [TII Falcon LLM License](https://huggingface.co/tiiuae/falcon-40b-instruct/blob/main/LICENSE.txt).** *Paper coming soon 😊.* ## Why use Falcon-40B-Instruct? * **You are looking for a ready-to-use chat/instruct model based on [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b).** * **Falcon-40B is the best open-source model available.** It outperforms [LLaMA](https://github.com/facebookresearch/llama), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1), [MPT](https://huggingface.co/mosaicml/mpt-7b), etc. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). * **It features an architecture optimized for inference**, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)). 💬 **This is an instruct model, which may not be ideal for further finetuning.** If you are interested in building your own instruct/chat model, we recommend starting from [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b). 💸 **Looking for a smaller, less expensive model?** [Falcon-7B-Instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) is Falcon-40B-Instruct's small brother! ```python from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model = "tiiuae/falcon-40b-instruct" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", ) sequences = pipeline( "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:", max_length=200, do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` # Model Card for Falcon-40B-Instruct ## Model Details ### Model Description - **Developed by:** [https://www.tii.ae](https://www.tii.ae); - **Model type:** Causal decoder-only; - **Language(s) (NLP):** English and French; - **License:** [TII Falcon LLM License](https://huggingface.co/tiiuae/falcon-7b-instruct/blob/main/LICENSE.txt); - **Finetuned from model:** [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b). ### Model Source - **Paper:** *coming soon*. ## Uses ### Direct Use Falcon-40B-Instruct has been finetuned on a chat dataset. ### Out-of-Scope Use Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful. ## Bias, Risks, and Limitations Falcon-40B-Instruct is mostly trained on English data, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online. ### Recommendations We recommend users of Falcon-40B-Instruct to develop guardrails and to take appropriate precautions for any production use. ## How to Get Started with the Model ```python from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model = "tiiuae/falcon-40b-instruct" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", ) sequences = pipeline( "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:", max_length=200, do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` ## Training Details ### Training Data Falcon-40B-Instruct was finetuned on a 150M tokens from [Bai ze](https://github.com/project-baize/baize-chatbot) mixed with 5% of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) data. The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer. ## Evaluation *Paper coming soon.* See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results. ## Technical Specifications For more information about pretraining, see [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b). ### Model Architecture and Objective Falcon-40B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token). The architecture is broadly adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), with the following differences: * **Positionnal embeddings:** rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864)); * **Attention:** multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)); * **Decoder-block:** parallel attention/MLP with a single layer norm. For multiquery, we are using an internal variant which uses independent key and values per tensor parallel degree. | **Hyperparameter** | **Value** | **Comment** | |--------------------|-----------|----------------------------------------| | Layers | 60 | | | `d_model` | 8192 | | | `head_dim` | 64 | Reduced to optimise for FlashAttention | | Vocabulary | 65024 | | | Sequence length | 2048 | | ### Compute Infrastructure #### Hardware Falcon-40B-Instruct was trained on AWS SageMaker, on 64 A100 40GB GPUs in P4d instances. #### Software Falcon-40B-Instruct was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.) ## Citation *Paper coming soon 😊.* ## License Falcon-40B-Instruct is made available under the [TII Falcon LLM License](https://huggingface.co/tiiuae/falcon-40b-instruct/blob/main/LICENSE.txt). Broadly speaking, * You can freely use our models for research and/or personal purpose; * You are allowed to share and build derivatives of these models, but you are required to give attribution and to share-alike with the same license; * For commercial use, you are exempt from royalties payment if the attributable revenues are inferior to $1M/year, otherwise you should enter in a commercial agreement with TII. ## Contact falconllm@tii.ae