Auto devices oobabooga reddit

Auto devices oobabooga reddit. 5 tokens per second or less. Learn how to fix common errors, optimize performance, and enjoy Oobabooga's features. gguf in a subfolder of models/ along with these 3 files: tokenizer. Here's Linux instructions assuming nvidia: 1. Check out the code itself for explanations on how to setup the backgrounds, or make any personal modifications :) Feel free to ask me questions if you don't understand something! I was wondering if you had any insight on how to squeeze every token/s out of your GPU. The first tokens of the answers are generated very fast, but then GPU usage suddenly goes to 100%, token generation becomes extremely slow or comes to a complete halt. "ExLlamav2" didn't work at all. I just picked the facebook one and immediately canceled the download. Mine looks like this on widows: --gpu-memory 10 7 puts 10GB on the rtx 3090 and 7GB on the 1070. 14 GiB already allocated; 0 bytes free; 3. It does skip the onboard graphics. I am trying to train a Lora using Oobabooga. Keep an eye on the output and see where the wall is in terms of tokens. The commands you see there can be amended to the webui. Under WSL2 Ubuntu 22. By default, the OobaBooga Text Gen WebUI comes without any LLM models. 7. You signed out in another tab or window. Error: libcuda. Try to shorten the max tokens and truncate them to way below 2048, maybe make messages around 150 tokens and Max prompt size 500 and slowly ramp up from there until you hit a wall. py --load-in-8bit --chat --wbits 4 --groupsize 128 --auto-devices . which is really impressive that it is beating the 70b models. 5. If you are facing problems with installing, running, or using Oobabooga, a local LLM that works with various models, you can find some helpful tips and solutions from other users in this subreddit. I'm running a fairly aging Nvidia GTX 1080, 8Gb DDR3 RAM, i5 2500K CPU and I'm getting 0. Use set to pick which gpus to use. I can not make it run with auto device at 16bit, but instead run it as 8bit. and where it says call python server. txt file for obabooga text gen put this in the file: --api --api-key 11111 --verbose --listen --listen-host 0. No fuss, no muss, it only asked me for the split - that was all. Hey! I created an open-source PowerShell script that downloads Oobabooga and Vicuna (7B and/or 13B, GPU and/or CPU), as well as automatically sets up a Conda or Python environment, and even creates a desktop shortcut. Can't use origninal CodeLlama-7B as OOM no matter how small the trainning parameters I set. bashrc in a text editor and add this line at the end: in fact I have 12 cards in that system. so. gpt4-x-alpaca is what I've been waiting for. 11K subscribers in the Oobabooga community. Learn how to create stories, characters, summaries, and more with Oobabooga. 3- in the CMD_FLAGS. You may have to reduce max_seq_len if you run out of memory while trying to generate text. 06 GiB already allocated; 73. I edited my start-webui. 1 token/sec). Whats up guys, i'm having trouble with the metaIx model as stated in my title. May 2, 2023 · Describe the bug Hello I use this command to run the model in GPU but its still run cpu, python server. Tried to allocate 394. It'll make no noise and keep your card below 70°C under load. I need to do the more testing, but seems promising. In this subreddit, you can find tips, tricks, and troubleshooting for using Oobabooga on various platforms and models. 81 MiB free; 15. bat but edit webui. zip from the Releases to install the UI And had to edit the start-webui. 5 tokens/s running GPT4 x Alpaca (dunno if this is model dependent), with the VRAM slider maxed out in the model tab, auto-devices on and loading as 8-bit (disk and CPU OP • 1 yr. Official subreddit for oobabooga/text-generation-webui, a Gradio web UI for Large In the Model tab, select "ExLlama_HF" under "Model loader", set max_seq_len to 8192, and set compress_pos_emb to 4. - I have tried different LoRA Rank, Learning Rate, Epochs, etc. I have a NVIDIA GeForce RTX 3060 Laptop GPU/6GB VRAM and 16 GB system RAM. I've tried auto-devices and both manually assigning memory amounts in Oobabooga, but nothing seems to work. Troubleshooting issues due to being unable to run Oobabooga : r/Oobabooga - Reddit. py--auto-devices --cai-chat" just type *--extension silero_tts formatted the same as the other flags (commands). bat and it should install silero before choosing to run the Pyg. bat and add your flags after "call python server. Check that you have CUDA toolkit installed, or install it if you don't. Add webui. You will also learn how to avoid some common errors that may occur during the process. A few weeks ago I setup text-generation-webui and used LLama 13b 4-bit for the first time. Even if I ignore the task manager physical location data and just stick to nvidia-smi, according to oobabooga, it’s the other way around. 1GB. Hope this helps! Loaded 33B model successfully. Here are instructions for you to load large models. py --auto-devices --cai-chat --wbits 4 --groupsize 128 --auto-devices --gpu-memory 5000MiB --no-stream --cpu-memory Actually, does look like it works. r/Oobabooga. 00 GiB total capacity; 3. Simple tutorial: Using Mixtral 8x7B GGUF in ooba. In the python server. - Low VRAM guide · oobabooga/text-generation-webui Wiki Hey folks. For reference, I'm used to 13B models generating at 2T/s, and 7B models at 4 T/s. In theory, yes; you should be able to get more tokens with the increased VRAM if you were running out of memory before. It’s used most commonly when training QLoRAs. I think it primarily targets college computing research labs and the mid sized ones for those of us with workstations Basically having a middle-schooler run a 6b on their gaming rig now can lead to them running a 30b by the time they're in high-school, and then they already know what they are doing in college when they have time in their colleges' advanced computing lab. I tried installing Oobabooga's Web UI but I get a warning saying that my GPU was not detected and that it falls back into CPU mode, how do I fix this? I'm having the exact same problem with my RTX 3070 8gb card using the one click install. However, I am now using --auto-devices and gpu-memory 3 and it appears to be working fine. removed model-menu and it did launch, but on the model tab the Model field still said 'none' and I even tried downloading 'eachadea/vicuna-7b-1. So I tried using AlekseyKorshuk_vicuna-7b with the "Transformers" and "ExLlamav2" loaders. 9-1. I'm having a similar experience on an RTX-3090 on Windows 11 / WSL. 7 tokens/s after a few times regenerating. ) I added a second 6000 Ada, and checked auto-devices in Oobabooga, but it still only tries to load into one GPU and I still get the CUDA errors. Reload to refresh your session. bat, cmd_macos. py --model ethzanalytics_mpt-7b-storywriter-sharded --load-in-8bit --listen --trust-remote-code. I have a 3090 and a 1070. It doesn't want to even look at the other GPUs I'm giving it. # Adding flags like --chat, --notebook, etc. 0 --listen-port 1234. py --wbits 4 --model_type LLaMa --chat --auto-devices. This is a video of the new Oobabooga installation. with AutoGPT: TypeError: Yi isn't supported yet. bat. You switched accounts on another tab or window. Follow all instructions - which are basically, paste the address into the ooba model download, download it, load, bingo. py --auto-devices --cai-chat --load-in-8bit. Download the 1-click (and it means it) installer for Oobabooga HERE . Once the model is selected, it should automatically choose the transformers' backend to load. Run iex (irm vicuna. model using the transformer loader and auto devices enabled call python server. 3060 12gb - Guanaco-13b-gptq: Output generated in 21. The more of the GGUF model you can fit on your GPU, the better. But didn't work even with these arguments: --auto-devices --chat --model-menu --wbits 4 --groupsize 128 --no-stream --gpu-memory 5 --no-cache --pre_layer 10 --chat Kinda just gave up right now. There is mention of this on the Oobabooga github repo, and where to get new 4-bit models from. (probably larger for big ones like 30B) As well as your OS, in my experience Linux speeds up my Tps 1. 27 seconds (17. It loads fine into my 4090 graphic card but when I type to it, it just says "is typing" but never actually responds and my gpu usage stays at a 100%. run nvidia-smi first from the windows command line to make sure it can see them. So I've tested --auto-devices and gpu-memory 4. 26 GiB Oobabooga has been upgraded to be compatible with the latest version of GPTQ-for-LLaMa, which means your llama models will no longer work in 4-bit mode in the new version. When I load an AWQ I have an rtx 2060 and I downloaded the necessary files. file: python server. On a 70b parameter model with ~1024 max_sequence_length, repeated generation starts at ~1 tokens/s, and then will go up to 7. Tutorials on Youtube made it seem like 4gb was enough so I thought with my 6 I was good. py --cai-chat --gpu-memory 8 --no-stream. sh, or cmd_wsl. play with nvidia-smi to see how much memory you are left You can ignore the pytorch files. py file flag --model-dir models2. It's very quick to start using it in ooba. Mine looks like python server. 0. Don't forget to save :) 5. py --model gpt4-x-alpaca-13b-native-4bit-128g --wbits 4 --groupsize 128 --auto-devices --pre_layer 26 --cai-chat There is another fag to tell the GPU how much memory to use, and if you are on Linux youculd use deepspeed read about the flags on the Oobabooga repo. For each node you want to create launch the server like this lollms-server --host (the host name: 0. ht) in PowerShell, and a new oobabooga-windows folder Just install the one click install and make sure when you load up Oobabooga open the start-webui. Open . Training a new model : r/Oobabooga - RedditAre you interested in creating your own text generation model using oobabooga? In this post, you will find some tips and tricks on how to train a model from scratch, using raw text files and the GUI. adding model for oobabooga interaface. Step four: Go to the training tab. Tried to allocate 64. Of course, give or take a few 10% for these speeds. cuda. How to pre-layer and how to make a 30b4bit model? - RedditIf you are interested in creating your own 30b4bit models using oobabooga, this post will guide you through the steps of pre-layering and modeling. Just a 1660 TI with 6gb. Once that is done, boot up download-model. py --model_type Llama --xformers --api --loader llama. No load on other cards but the first one. Put a dummy model in the default models folder. tc. P100 since it has half precision support so it’ll run much faster. Quantized 13B models on 12GB VRAM. n-gpu-layers depends on the model. 12 votes, 16 comments. app, conn)(scope, receive, send) | File "F:\Oobabooga\installer_files\env\Lib\site-packages\starlette\_exception_handler. 1' again using the "Download custom model or LoRA" feature which had all the progress bars in the terminal, but nothing. Still i got some memory issues : torch. I just tried running the 30b WizardLM model on a 6000 Ada with 48gb of RAM, and I was surprised that apparently that wasn't enough to load it (it gives me CUDA out of memory errors. I tried --auto-devices and --gpu-memory (down to 9000MiB), but I still Oobabooga startup params:--load-in-8bit --auto-devices --gpu-memory 23 --cpu-memory 42 --auto-launch --listen I still have a problem getting around some issues, likely caused by improper loader settings. It was very underwhelming and I couldn't get any reasonable responses. Then plug both fans into the motherboard. Specs: 6 Gb VRAM, 16 Gb RAM, Windows 10. Mar 19, 2023 · Alright. json. Still out of memory immediately. cpp --n-gpu-layers 55 --n_ctx 6000 --compress_pos_emb 3--auto-devices --n_batch 300 --model <your model here> --threads 5 --verbose. I'm looking for some tips how to set them optimally. Open oobabooga folder -> text-generation-webui -> css -> inside of this css folder you drop the file you downloaded into it. bat file to have a small pre_layer and it worked for me. https://ibb. I'm wondering if ft2 works and if this happens with other people's exllamav2?, Any advice on this? python server. You now look for this block of code. Or whatever the path is to your folder. py", line 62, in __call__ | await wrap_app_handling_exceptions(self. 9 tokens per second on a 3080? Is this accurate? If so that seems very slow On GGGM 30b models on an i7 6700k CPU with 10 layers offloaded to a GTX 1080 CPU I get around 0. You could remove the -auto-devices option I suppose but that doesn't do anything for me in the first place. Baseline is the 3. py --auto-devices --chat --model-menu --wbits 4 --groupsize 128 Error: OutOfMemoryError: CUDA out of memory. co/PDSmh1Y. 00 MiB (GPU 0; 15. ago. My concern is the performance. 0. Congrats, it's installed. If you ever need to install something manually in the installer_files environment, you can launch an interactive shell using the cmd script: cmd_linux. In the options, check the ones for auto devices, load in 4bit, and double quant. 0 if you want it to be accessible anywhere) --port (the specific port of the - Loader: transformer with auto-devices and disable_exllama checked - Base model: tried TheBloke/CodeLlama-13B-Instruct-GPTQ and 7B. py --chat --gpu-memory 6 6 --auto-devices --bf16 usage: type processor memory comment cpu 88% 9G GPU0 16% 0G intel GPU1 19% 0G nvidea I've used Oobabooga with Pygmalion and Vicuna for some weeks and ran into problem when trying to run models such as wizardLM-7B-GPTQ-4bit-128g and TheBloke_stable-vicuna-13B-GPTQ. I have no idea why it doesn't see it. For the python call I am using these, call python server. so: cannot open shared object file: No such file or directory". 90 GiB total capacity; 15. I use oobabooga UI as it's the most comfortable for me and lets me test models before I've been messing around with trying to get deepspeed running the past day or two, and I think I'm noticing that it loads models correctly more often when I do not use the oobabooga flag "--deepspeed", such as: deepspeed --num_gpus=1 server. py--auto-devices --chat --wbits 4 --groupsize 128 Supposed to be really good, probably better than vicuna 7B Yeah I don't know anything about ChromaDB either, but I'd rather run everything locally instead of using pinecone so I hope all the wrinkles get smoothed out soon! When using exllama inference, it can reach 20 token/s per second or more. GPU not detected, Oobabooga web UI. Join the discussion and share your results with Apr 9, 2023 · I used the oobabooga-windows. . View community ranking In the Top 10% of largest communities on Reddit. This 13B model was generating around 11tokens/s. As to stop using 1070 without disabling it outright, I don't know how to do that. - Home · oobabooga/text-generation-webui Wiki. That's a default Llama tokenizer. Official subreddit for oobabooga/text-generation-webui, a Gradio web UI for Large Language Models. 4- load up obabooga textgen and then load your model (you can go back to autogen and your model and press the "test model" button when the model is Official subreddit for oobabooga/text-generation-webui, a Gradio web UI for Large Language Models. For running llama-30b-4bit-128g. Any help would be appreciated, thank you. py --cai-chat --auto-devices --no-stream replace --auto-devices with --gpu-memory GPU_MEMORY and replace "GPU_MEMORY" with how much you want to allocate. Modes & Routines is a service for automatically changing your device features and settings according to the time and place and also recommends useful features. I tried all kind of parameter sets, no matter how I set them, I Oobabooga startup params: --load-in-8bit --auto-devices --gpu-memory 23 --cpu-memory 42 --auto-launch --listen. I checked the INSTRUCIONS. with Transformers: ImportError: Found an incompatible version of auto-gptq. py--auto-devices My suspicion is that the Oobabooga text-generation-webui is going to continue to be the primary application that people use LLaMA through - and future LLaMA derivatives, and other open models as they come out - hence on the choice of whether to start a Oobabooga sub or a LLaMA-AI sub, I'd go with the former, so as to cover the whole growing On a 4090FE w/ 128GB of DDR4 with “ —auto-devices —pre_layer 300” to utilize the full 64GB or shared VRAM available for a total of 88GB of VRAM If you’re looking to shrink it you could use the latest and greatest (at time of writing) , or just look around for a 4bit quantized version I see Yi-34 is currently the leader in the LLM leader board. Its quite slow though. json, and special_tokens_map. com) Using his setting, I was able to run text-generation, no problems so far. No need to install it by number. 8. The trend seems to be "Intel = fast, Amd = slow". No slider, no auto devices, no nothinggo check it out. localhost\Ubuntu-22. 04\home\ <username>. Then, select the llama-13b-4bit-128g model in the "Model" dropdown to load it. Remember that deepspeed is for scaling over multiple GPUs and even multiple host systems in a datacenter. Having a larger context size can decrease your Tps by 10-50% depending on the model. When using the flag, it seems to follow the wrong call python server. 5-1 token/second. 81 tokens/s, 379 tokens, context 21, seed 1750412790) Output generated in 70. Its NVIDIA GPU, and AMD CPU. def run_model(): Jun 7, 2023 · You signed in with another tab or window. call python server. ) and quantization size (4bit, 6bit, 8bit) etc. For me, these were the parameters that worked with 24GB VRAM Download oobabooga/llama-tokenizer under "Download model or LoRA". OobaBooga is up to date as of today. I was planing on using this as a way to see how two gpus work before buying a second 3060 12gb, or two Tesla P40. a blower style fan will make you regret your life's decisions. it/129w4qh. The AI response speed is quite fast. I may fiddle with higher ones but 10 worked for now. 7 token/s its a lot slower than KoboldAI though (1. Go to repositories folder. But that's not as much compared to the 4-6x The text that pops up when you mouse over it states: "This binding allows you to use one or multiple Lord of LLMs services on your network or through internet. The first nvidia that is. Step three: Load the model. I tried --auto-devices but nothing changes. My 3090 is detected as device 0 and the 3060ti is detected as device 1. py --auto-devices --chat" In the new oobabooga, you do not edit start_windows. bat and select 'none' from the list. py --auto-devices --cai-chat --wbits 4 --groupsize 128 --extension send_pictures You'll see a new window on the main page of the Oobabooga UI and you can just drop pictures inside the window. 128 --gpu-memory 6000MiB --pre_layer 30 --auto-devices --cai Jan 15, 2024 · The OobaBooga WebUI supports lots of different model loaders. Auto makes no difference. Please use the To be completely honest, the last bigger update already felt like a downgrade to me and this one completely broke it. Hi, I have an old 8GB Geforce GTX 1070 card that still runs and generates AUTOMATIC 1111 nicely. py --threads 5 --chat --model AlekseyKorshuk_vicuna-7b. py --load-in-8bit --auto-devices --gpu-memory 3500MiB --chat --wbits 4 --groupsize 128 --no-cache. I don't think you need another card, but you might be able to run larger models using both cards. RTX 3060ti is device 0 and RTX 3090 to be Device 1. Using JCTN/pygmalion-13b-4bit-128g on a 8GB VRAM card. Place your . It's quite literally as shrimple as that. I have my data into Alpaca format (intructtion, input, output) python server. It's probably not a good idea to use it if it's not required though because it adds unnecessary complexity. py --model vicuna-13b --load-in-8bit --auto-devices --listen --public-api --xformers --gpu-memory 22 22 22 22. Bump your vram slider up to 7GB. Now my card has about 6gb vram and in order to run this, I needed the low spec arguments. CUDA out of memory for 6 Gb VRAM, Ooba. 7 t/s To access the web UI from another device on your local network, you will need to configure port forwarding: netsh interface portproxy add v4tov4 listenaddress=0. TXT and it said this. There are most likely two reasons for that, first one being that the model choice is largely dependent on the user’s hardware capabilities and preferences, the second – to minimize the overall WebUI download size. I tried manually reversing the Apr 15, 2023 · In theory you should only have a performance hit if the model can't fit entirely onto your main gpu, in which case the auto-devices flag is mandatory anyway. Win 10, latest Nvidia drivers. cpp (GGUF), Llama models. Now open start-webui. A 4 bit version of a 7B should run on a 3060. I actually want oobabooga to use the 3060ti when running 30B models so I can have a larger token size. start-webui. Still, with gpu memory set to 3 it works a lot better than previously. I have a GTX 1650 SUPER with 4GB of VRAM. When it asks you for the model, input mayaeary/pygmalion-6b_dev-4bit-128g and hit enter. I've not been successful getting the AutoAWQ loader in Oobabooga to load AWQ models on multiple GPUs (or use GPU, CPU+RAM). Trying to set flags for CUDA Visible device to either 0 or 1 makes no difference. The script uses Miniconda to set up a Conda environment in the installer_files folder. set CUDA_VISIBLE_DEVICES=0,1. Supports transformers, GPTQ, AWQ, EXL2, llama. 00 MiB (GPU 0; 4. 16 tokens/s, 993 tokens, context 22, seed 649431649) Using the default ooba interface, model settings as described in the ggml card. Did I write it right? Dec 31, 2023 · The instructions can be found here. I added the --load-in-8bit , --wbits 4, --groupsize 128 and changed the --cai-chat to --chat I used the Low VRAM guide call python server. But at the cost of a lot of copying the data to and from devices. Edit the "start" script using a text editor and add the desired flags. Oh and speedjaw dropping! What would take me 2-3 minutes of wait time for a GGML 30B model takes 6-8 seconds pause followed by super fast text from the model - 6-8 tokens a second at least. sh, cmd_windows. gpt4-x-alpaca-13b runs very slow - 0. At this point I waited for something better to come along and just used ChatGPT. The model loads fine, and I can train at relatively low rank and batches. bat to make it work. However, when I switched to exllamav2, I found that the speed dropped to about 7 token/s, which was slowed down. Been using this guide: https://redd. 2. OutOfMemoryError: CUDA out of memory. I'm curious as to why you don't want it. On the other hand if you just want large VRAM then just get M40 24GB rather than a P40 as they’re a lot cheaper and not much slower since both don’t do half precision. Make a models2 folder and move everything there. Oobabooga is a web UI and API extension for Pygmalion AI, a powerful text generation tool based on GPT-3. Oobabooga has been upgraded to be compatible with the latest version of GPTQ-for-LLaMa, which means your llama models will no longer work in 4-bit mode in the new version. Model: Pygmalion 6B, 4-bit precision. Do you get good results from original model, or is my Welcome to our community of Modes & Routines with Routines +! Feel free to post and comment on your routines, suggestions, queries etc. You should just be able load the GPTQ file in Transformers directly, without any of that other than perhaps auto-devices, as long as its config file is correct. true. However I have unused Vram available, aprox 4 gigs on one card, the other is just about maxed out. I don't even know where to put my parameters like --auto devices in this new hierarchy and there's still no save button for the character page for some reason. Same run can be done by the gui. With 0. The git commit version is b040b41. Activate conda env. Apr 20, 2023 · Running the following commands throws an error: python server. Its not a complete failure as to my surpise the model does work with a gpu that is 3ft from the motherboard through a usb 3. So I loaded up a 7B model and it was generating at 17 T/s! I switched back to a 13B model (ausboss_WizardLM-13B-Uncensored-4bit-128g this time) and am getting 13-14 T/s. A Gradio web UI for Large Language Models. No worries, 12 GB doesn't seem to run anything bigger than 13B models so you're not missing out on much. python server. 11 seconds (14. I am running a 4xA10GPUs, each with 24Gb VRAM. But I get a bunch of errors which I don't understand. Fix: Browse into: \\wsl. --no-cache might work well too but will slow everything tremendously. Oobabooga seems to have run it on a 4GB card Add -gptq-preload for 4-bit offloading by oobabooga · Pull Request #460 · oobabooga/text-generation-webui (github. I could only run this 7b from all which i tried TheBloke/WizardLM-7B-uncensored-GPTQ. co/YLsYyRg. One other thought to throw into this mix, is the 12GB variant of the RTX 3060. Weirdly, inference seems to speed up over time. *** Multi-LoRA in PEFT is tricky and the current implementation does not work reliably in all cases. py", line 64, in wrapped_app | raise exc Most models I've tried are able to take advantage of multiple GPU's. 35 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. bat file in a text editor and make sure the call python reads reads like this: call python server. Am I missing something? The largest card I have is 80gb, and that's not enough to load a 65b model out of the box. 0 cable. 13b models as GPTQ run fine. Troubleshooting: If you will use 4-bit LLaMA with WSL, you must install the WSL-Ubuntu CUDA toolkit, and it must be 11. Apr 19, 2023 · In the old oobabooga, you edit start-webui. However, the oobabooga just loads, but the assistant doesn't answer any questions. py file, toward the bottom, but you shouldn't have to worry about that unless you need to enable more advanced options. The Bloke mentions "These GPTQ models are known to work in the following inference servers/webuis. One-line Windows install for Vicuna + Oobabooga. Feb 23, 2023 · A Gradio web UI for Large Language Models. reddit's new API changes kill third party apps that offer accessibility features, mod tools, and other features not found in the first party app. Is it supported? I read the associated GitHub issue and there is mention of multi GPU support but I'm guessing that's a reference to AutoAWQ and not necessarily its integration with Oobabooga. I tried that and it didn't work. 3. CSCareerQuestions protests in solidarity with the developers who made third party reddit apps. Just make sure the "--auto-devices" argument is set or manually set the limit for the GPU from the webui. 5x. With the newest version of the installer you just need to use the same start script you used to install it. I am trying to load quantized 13B models on an RTX 4070 with 12GB VRAM. | File "F:\Oobabooga\installer_files\env\Lib\site-packages\starlette\middleware\ exceptions. Download fan control from github and manage the fans according to the P40's sensors. bat guts: call python server. How many layers will fit on your GPU will depend on a) how much VRAM your GPU has, and B) what model you’re using, particular the size of the model (ie 7B, 13B, 70B, etc. model, tokenizer_config. necile. WebUI: Oobabooga. You may want only --auto-devices, without deepspeed. I can load the said model in oobabooga with the cpu switch on my 8GB VRAM card. Put both fans on top of the P40 heatsink to blow onto it. py, which should be in the root of oobabooga install folder. • 9 mo. With the "Transformer" loader and the "auto-devices" option, I was able to get it to work, but it was really slow at 0. py" like "call python server. I still have a problem getting around some issues, likely caused by improper loader settings. I am loading a 70B model via transformers with the flags "auto-devices", "load-in-4bit", and "use_double_quant" flags. 0 listenport=7860 connectaddress=localhost connectport=7860. But when I enter something, there is no response and I get this error: 2023-07-02 09:03:45 INFO:Loading JCTN_pygmalion-13b-4bit-128g 2023-07-02 09:03:45 WARNING:The model weights are not tied. So using deepspeed on a single machine with a single GPU just gives a performance penalty. Today I downloaded and setup gpt4-x-alpaca and it is so Thanks but I got nothing like a 4090. py--auto-devices --chat --wbits 4 --groupsize 128 --pre_layer 12 I have an RTX 3060, 16Gb ram, any tips on how to make it faster? comments sorted by Best Top New Controversial Q&A Add a Comment No, “load in 4 bit” and “double quant” is for when you have FP16 weights and you want to do quantisation of those weights on the fly. This takes precedence over Option 1. I have 3 different auto gpt type things installed all with There's an easy way to download all that stuff from huggingface, click on the 3 dots beside the Training icon of a model at the top right, copy / paste what it gives you in a shell opened in your models directory, it will download all the files at once in an Oobabooga compatible structure. And switching to GPTQ-for-Llama to load the OR, How do I get oogabooga to reinitialize the GPUs with the correct device ID without reinstalling everything? I would even settle for a way to just swap GPU IDs in oogabooga settings some how. 04 you might encounter a crash with this error: "Could not load library libcudnn_cnn_infer. I put this entry in the bat. You will also learn how to customize the settings and parameters to suit your needs and preferences. qc rt ao fb lq zc sb jd ye gg