Llama 30b 4bit
Llama 30b 4bit. LLaMA is a Large Language Model developed by Meta AI. 0-GPTQ with text-generation-webui. like 25. Quantized using --true-sequential and --act-order optimizations. And the one *you* want is: openassistant-llama-30b-4bit. Apr 15, 2023 · Since llama 30b is properly the best model that fits on an rtx 3090, I guess, this model here could be used as well. py --grad_chckpt --flash_attention huggyllama/llama-30b merged with serpdotai/llama-oasst-lora-30B. safetensors 9 months ago; openassistant-llama-30b-ggml-q4_1. Made a small table with the differences at 30B and 65B. getfullargspec(make_quant). python setup_cuda. llama-30b-SuperHOT-4bit. Inference Endpoints. 4GB so the next best would be vicuna 13B. Download the 4-bit model of your choice and place it directly into your models folder. json. This really surprised me, since the 3090 overall is much faster with stable diffusion. 테스트 해줄사람도 필요하고 해서 올려본다 써보고 의견같은거 있으면 밑에 디스코드로 연락주면 됨 Vicuna 13b 4bit works extraordinarily well, in my experience even beating the unquantified version, and it’s very fast. Non of the 30b models do well as far as I have tested so far, sadly. llama-30b-4bit. ) So being a little weaker isn't too surprising. Example: " gpt4-x-alpaca-30b-4bit. make_quant_kwargs = {. It's reasoning abilities are roughly on par with other good 30B LLaMa-based models. Their eval shows it's a little weaker than LLaMA-"30B" (which would actually be called 33B if it weren't for a typo in the download), which makes sense, since in the blogpost they note that: MPT-30B trains 30B params on 1T tokens. This does not support llama. It takes about 180 seconds to generate 45 tokens(5->50 tokens) on single RTX3090 based on LLaMa-65B. bat again, and it should download and load the new model. As you probably have 2 channels, you end up at something like 2*8*3600 MB/s or 57. Replace. These files are GGML format model files for Meta's LLaMA 30b. FAIR should really set the max_batch_size to 1 by default. There is another high-speed way to download the checkpoints and tokenizers. download --model_size 7B. like 6. I was also impressed with it. 理论上只要有 64GB DRAM 就能运行 30B 的量化模型,但是最好还是找块 RTX 3090 及以上的显卡。. To download from a specific branch, enter for example TheBloke/OpenAssistant-SFT-7-Llama-30B-GPTQ:gptq-4bit-32g-actorder_True; see Provided Files above for the list of branches for each option. 8. GGML files are for CPU + GPU inference using llama. Model card Files Files and versions Community Mar 5, 2023 · This repository contains a high-speed download of LLaMA, Facebook's 65B parameter model that was recently made available via torrent. This is exactly why I keep the HF uncompressed pytorch files around! Time to get guanaco-65b and see if I can force it to run almost entirely from VRAM MetaIX/GPT4-X-Alpasta-30b-4bit · Hugging Face. It does a bit more refusals complaining about insufficient information or inability to perform a task, which might either be a pro or a cons for you. Jul 18, 2023 · At 4bit quantisation, a 7B model can run on an 8GB GPU. Not sure what you mean by "model type should be llama". py c:\llama-30b-supercot c4 --wbits 4 --act-order --true-sequential --save_safetensors 4bit. It's 32 now. 즉, Models 폴더 안에 llama-30b-hf, 그리고 llama-30b-4bit (서브폴더 없이 바깥에) 이렇게 둘 다 있어야 함. < llama-30b-4bit 1st load INFO:Loaded the model in 7. Quantized using --true-sequential and --groupsize 128 optimizations. Information. Apr 13, 2023 · From PyTorch docs: The model is replicated on all the devices; each replica calculates gradients and simultaneously synchronizes with the others using the ring all-reduce algorithm. . safetensors Its been updated yesterday so I removed the old . model (. Just don't bother with the powershell envs Apr 1, 2023 · LLaMa-Storytelling-4Bit. Installation instructions updated on March 30th, 2023. Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. GPU: RTX 4090. huggingface. Really though, running gpt4-x 30B on CPU wasn't that bad for me with llama. I tried the smallest one (125m I think) and for the size it's shocking how good it is. This guide actually works well for linux too. py. 30b 4bit need way more spaces then 13b, try to use --pre_layer PRE_LAYER to offload. For older cards that failed to compile: Upload openassistant-llama-30b-4bit. Reload to refresh your session. For instance, one can use an RTX 3090, an ExLlamaV2 model loader, and a 4-bit quantized LLaMA or Llama-2 30B model, achieving approximately 30 to 40 tokens per second, which is huge. 632 Online. -research/llama-30b-hf-int4. 64; C4: 6. LLaMA-30B fits on a 24GB* consumer video card with no output performance loss, beating GPT-3 175B. These impact the VRAM required (too large, you run into OOM. Preface: zero Python experience. Merge of SuperHOT-LoRA-prototype and llama-30b. 30 tokens/s. I used their instructions to process the xor data against the original Llama weights and verified all checksums at each step. 53 seconds. cpp and libraries and UIs which support this format, such as: KoboldCpp, a powerful GGML web UI with full GPU acceleration out of the box. cpp,目前似乎只能用 CPU 而不能充分利用 GPU 或者 Accelerate。. You can also export quantization parameters with toml+numpy format. Model card Files Files and versions Community 5 main LLaMa-Storytelling-4Bit 30B-ggml. GPTQ: 2 quantized versions. Yes, I place the model in a 5 years old disk, but both my ram and disk are not fully loaded. raw history blame contribute delete Under Download custom model or LoRA, enter TheBloke/WizardLM-30B-uncensored-GPTQ. SuperHOT is a new system that employs RoPE to expand context beyond what was originally possible for a model. The result is that the smallest version with 7 billion parameters has similar performance to GPT-3 with 175 billion parameters. However, to run the larger 65B model, a dual GPU setup is necessary. 6 GB/s memory speed under ideal streaming conditions. Some have difficulty even with full 8bit quantization; others you can go to 4bit relatively easily. zip. GPT4 Alpaca LoRA 30B - GPTQ 4bit 128g This is a 4-bit GPTQ version of the Chansung GPT4 Alpaca 30B LoRA model. This is an experimental new GPTQ which offers up to 8K context size. Train 4-bit loras with Autograd and hopefully soon AutoGPTQ; exLlama support (compute 7 and up for benefits) more parameters from UI for remote hosts Mar 13, 2023 · 어제 4090 가지고 30B 4bit 구동 어렵게 시키는 친구보고 이런게 필요하겠구나 싶어서 만들어봄 openai 토글 키면 gpt-3. New: Create and edit this model card directly on the website! We’re on a journey to advance and democratize artificial intelligence through open source and open science. OpenCL). bin. model="llama-7b" I've tested changing it to 13b and that worked without problem. Both 4bit-128g and 4bit non-groupsize versions are on my repo as well. LlaMa is But Galactica has massive potential if fine tuned with something like Open-Assistant dataset, and can hold a conversation based on the knowledge it's ingested. cpp/ggml supported hybrid GPU mode. Quantized models allow very high parameter count models to run on pretty affordable hardware, for example the 13B parameter model with GPTQ 4-bit quantization requiring only 12 gigs of system RAM and 7. Alpaca 30B 4-bit working with GPTQ versions used in Oobabooga's Text Generation Webui and KoboldAI. LoLLMS Web UI, a great web UI with GPU acceleration via the Feb 2, 2024 · This GPU, with its 24 GB of memory, suffices for running a Llama model. The gpt4-x-alpaca 30B 4 bit is just a little too large at 24. 2023. For instance, if you have DDR4 memory at, say, 3600 MT/s, that means there is 8*3600 megabytes that can be streamed from RAM to CPU per channel. Skip to content Information. 51 LLaMA Model Card Model details Organization developing the model The FAIR team of Meta AI. After changing, just close the webui console and run docker_start. After narrowing it down, I can confirm the issue began with the following commit: 113f94b The problem is not the new transformers version though, at least not directly. Multi-GPU support[2] means LLaMA-65B, rivaling PaLM-540B, runs on 2x3090. I'll also comment that, if you are currently building a consumer Mar 13, 2023 · I'm running 30B in 4bit on my 4090 24 GB + Ryzen 7700X and 64GB ram. Prompting You should prompt the LoRA the same way you would prompt Alpaca or Alpacino: Below is an instruction that describes a task, paired with an input that provides further context. Deploy. Below approximately 1000 tokens returned it will use <24GB VRAM, but at 1000 Use in Transformers. Mar 10, 2023 · 아 이거 왜 자꾸 모델 못 찾겠다면서 에러나나 했더니 예를 들어 30B돌리고 싶으면 30B-HF 모델도 있어야 함. Basically, 4-bit quantization and 128 groupsize are recommended. Mar 4, 2023 · The most important ones are max_batch_size and max_seq_length. I wish huggingface had a way to filter models by parameter count or even VRAM usage so models with odd numbers can be found easier. 58 seconds. Apr 11, 2023 · Same issue loading Llama 30b 4bit 128g models on my 3090. 如果对 Hyper-V Jul 31, 2023 · Quantize your own LLMs using AutoGPTQ. Mar 22, 2023 · Even with the extra dependencies, it would be revolutionary if llama. args. Tweet by Tim Dettmers, author of bitsandbytes: Super excited to push this even further: - Next week: bitsandbytes 4-bit closed beta that allows you to finetune 30B/65B LLaMA models on a single 24/48 GB GPU (no degradation vs full fine-tuning in 16-bit) - Two weeks: Full release of code, paper, and a collection of 65B models. It ignores --disk and --cpu i think, just loading to vram and getting errors. Top 2% Rank by size. (Note: LLaMA-13B ran at 0. It was then quantized to 4bit, groupsize 128g, using GPTQ-for-LLaMa. Model version This is version 1 of the model. Speed was: `Output generated in 424. cpp or any other cpp implemetations, only cuda is supported. Had to use double-quant to not OOM on 30b. (Discussion: Facebook LLAMA is being openly distributed via torrents) It downloads all model weights (7B, 13B, 30B, 65B) in less than two hours on a Chicago Ubuntu server. 30b uses 17. Mar 29, 2023 · The answer right now is LLaMA 30b. pickle. Edit model card. Click Download. 如果是 Apple Silicon 建议使用 llama. Yes, you will have to wait for 30 seconds, sometimes a minute. It is the result of quantising to 4bit using GPTQ-for-LLaMa. Trying to get LLaMa 30B 4bit quantized to run with 12GB of vram and I'm hitting OOM since the model is a bit more than 16gb Is it possible to use offloading to load a percentage of the model to cpu using GPTQ? LLaMA-13B, rivaling GPT-3 175B, requires only 10GB* of VRAM with 4bit GPTQ quantization. In the Model dropdown, choose the model you just downloaded: upstage-llama-30b-instruct-2048-GPTQ. You switched accounts on another tab or window. 31 seconds (3. ago. There's a solution here . Subreddit to discuss about Llama, the large language model created by Meta AI. This is the 4-bit GPTQ quantized model of OpenAssistant LLaMA 30B SFT 7. Not even sure if the rank in cutoff-8192 is correct (think it should be Mar 11, 2023 · Since the original models are using FP16 and llama. co/decapoda. py install is deprecated. and max_batch_size of 1 and max_seq_length of 1024, the table looks like this now: May 14, 2023 · Describe the bug Follow the instructions in project's document, but with LLaMA-30B-4bit-128g model I can only got meaningless answer, and also the GPU will keep running with no outputs. I did. I'll try this 30B with high hopes. If you will use 7B 4-bit, download without group-size. (1. Model type LLaMA is an auto-regressive language model, based on the transformer architecture. Apr 1, 2023 · You signed in with another tab or window. But if you have any issues with it, it's recommended to update to the new 4bit torrent or use the decapoda-research versions off of HuggingFace or produce your own 4bit weights. example: Loading llama-30b-4bit-128g. 50; Groupsize version is here: https://huggingface. WizardLM is also good and fast but i only 7b is out; a 13b version was posted somewhere but I haven’t tested it. This seems very good so far. 14047 Basically the TLDR is that performance drops harshly if you go lower than 4bit precision on the 8B model. These implementations require a different format to use. Make sure you only have ONE checkpoint from the two in your model directory! Edit model card. download. 100694179534912 oobabooga/text-generation-webui in githubhardware config: i7-12700K, RTX4090, 96GB-DDR4, 2TB SSD Check the following paper if you want to see a comprehensive comparison of different quantisations for llama-3: arxiv:2404. 84 seconds. 20. There is some research that suggests 3bit might be the useful limit, with rarely certain 2bit models. 5B params on 1. Updated the ggml quantizations to be compatible with the latest version of llamacpp (again). 21. These files are GGML format model files for Ausboss' Llama 30B SuperCOT. In text-generation-webui\modules\GPTQ_loader. The model comes in different sizes: 7B, 13B, 33B danger 11-3-23 There's a new torrent version of the 4bit weights called "LLaMA-HFv2-4bit". So 30B may be quite slow in Colab. Text Generation. safetensors Quantization: No guarantees for output quality, simply uploading what I have so others can play around with it. 7B-4bit and 13B-4bit works great Apr 5, 2024 · Offloading with llama_inference_offload, fastest multi-gpu besides exllama; Autograd + quant_attn beating Autogtpq on P6000! Only load one 4bit lora at a time and apply with no loras before switching. model should be from the Huggingface model folder of the same model type). 5 GB of VRAM. 26. https://. It was created by merging the LoRA provided in the above repo with the original Llama 30B model, producing unquantised model GPT4-Alpaca-LoRA-30B-HF. The links for the updated 4-bit models are listed below in the models directory section. LLaMA-13B-4bit-128g. 📚 愿景:无论您是对Llama已有研究和应用经验的专业开发者,还是对Llama中文优化感兴趣并希望深入探索的新手,我们都热切期待您的加入。在Llama中文社区,您将有机会与行业内顶尖人才共同交流,携手推动中文NLP技术的进步,开创更加美好的技术未来! Edit model card. 5랑 하는거고 끄면 llama-30B-4bit 이랑 챗하는거임. Env: Windows 10 x64. In summary: Inference performance: 4bit > 8bit > fp16 (as the code looks to be primarily memory-bound, with only a 50% performance increase from going from 8 cores to 16 cores on my 16 core / 32 hyperthread Ryzen 1650X) If you haven't already done so, create a model folder with the same name as your model (or whatever you want to name the folder) Put your 4bit quantized . Model. 24 seconds. gptq_args = inspect. Mar 21, 2023 · 107 17,974 7. pt files and grabbe Mar 12, 2023 · This issue is perhaps misnamed, now, as 8bit will likely improve quality over 4bit but not performance. There are four models (7B,13B,30B,65B) available. python llama. pre_layer is set to 50. License: other. 1 Jupyter Notebook. pt or . This model is under a non-commercial license (see the LICENSE file). Model card for Alpaca-30B This is a Llama model instruction-finetuned with LoRa for 3 epochs on the Tatsu Labs Alpaca dataset. MetaIX/OpenAssistant-Llama-30b-4bit & TheBloke/wizardLM-13B-1. The old "LLaMA-4bit" torrent may be fine. 4T tokens. Use in Transformers. Ausboss' Llama 30B SuperCOT GGML. I tried to get gptq quantized stuff working with text-webui, but the 4bit quantized models I've tried always throw errors when trying to load. < llama-30b-4bit 2nd load. We would like to show you a description here but the site won’t allow us. py --auto-devices --gpu-memory 20 --load-in-4bit --cai-chat --listen --extensions gallery llama_prompts --model llama-30b-4bit LocalLlama. I have a P6000 (24g VRAM) and such models load fine for me. llama. Vicuna is a high coherence model based Mar 10, 2023 · You should be able to run as large as LLaMA-30B in 8bit with Colab Pro. Just noticed this old post. Normally, fine-tuning this model is impossible on consumer hardware due to the low VRAM (clever nVidia) but there are clever new methods called LoRA and PEFT whereby the model is quantized and the VRAM requirements are dramatically decreased. Struggling to get 4bit working. alpaca-lora applied this successfully to fine-tuning LLaMa, and then exported / combined with the original model, later quantizing back to 4-bit so that it could be loaded by alpaca. Under Download custom model or LoRA, enter TheBloke/OpenAssistant-SFT-7-Llama-30B-GPTQ. This works out to 40MB/s (235164838073 Llama-2-7b-chat-hf. Update 05. safetensors Quantization: Llama30B-SuperHOT-4bit. When trying to run the new alpaca-30b-4bit-128g. It is llama? Secondly, I know it needs way more space than 13b but 3090 with 64gb system ram shouldn't have a problem with the 4bit 30b model should it? 2. 3 GB LFS Upload 3 files 8 months ago; Information. For instance, models/llama-13b-4bit-128g. 2022 and Feb. pyllama. Speed Comparison:Aeala_VicUnlocked-alpaca-30b-4bit. llama-30b-int4. It is possible to lora fine tune gptneox 20b in 8 bit. py:34: SetuptoolsDeprecationWarning: setup. 1. Especially good for story telling. This is achieved by converting the floating point representations for the weights to integers. You can run 30B 4bit on a high-end GPU with 24gb VRAM, or with a good (but still consumer grade) CPU and 32GB of RAM at acceptable speed. index. Downloads last month 0. The model will automatically load, and is now ready for use! 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. Loading the 13b model take few minutes, which is acceptable, but loading the 30b-4bit is extremely slow, took around 20 minutes. 7 GB LFS Mar 30, 2023 · LLaMA model. However, after a few exchanges, my GPU and VRAM are at 100% utilization and generation practically grinds to a halt Is anyone having success running this on a single 3090 / 4090 reaching the full llama-30b-supercot-4bit-128g-cuda / pytorch_model. ) Based on the Transformer kv cache formula. These files are GPTQ 4bit model files for Ausboss' Llama 30B SuperCOT merged with Kaio Ken's SuperHOT 8K. after generating some tokens asking to produce code I get out of memory errors using --gpu-memory has no effects server line python server. Use the one of the two safetensors versions, the pt version is an old quantization that is no longer supported and will be removed in the future. If you look at the 70B model you can get away with 3bit weight quantisation without a big hit to the performance. You signed out in another tab or window. These are SuperHOT GGMLs with an increased context length. However, the original weights quantized to int4 for fine tuning will be useful, too. The difference is pretty big. The increased context is tested to work with ExLlama, via the latest release of text-generation-webui. GPTQ-for-LLaMa. To download from a specific branch, enter for example TheBloke/WizardLM-30B-uncensored-GPTQ:gptq-4bit-64g-actorder_True; see Provided Files above for the list of branches for each option. The costs to have a machine of running big models would be significantly lower. Then quantized to 4-bit with GPTQ using oobabooga's fork. q3_k_m was better than q4_0 when testing ausboss/llama-30b-supercot. It may be possible, but there is no plan to support it at the moment. OpenAssistant LLaMA 30B SFT 7 GPTQ 4-bit. 66; PTB: 17. For the story-telling or role-playing abilities, you need to ask someone else, as I don't pay much I setup WSL and text-webui, was able to get base llama models working and thought I was already up against the limit for my VRAM as 30b would go out of memory before fully loading to my 4090. To run this model, you can run the following or use the following repo for generation. To download only the 7B model files to your current directory, run: python -m llama. This contains the weights for the LLaMA-30b model. This LoRA trained for 3 epochs and has been converted to int4 (4bit) via GPTQ method. I've been following the 30b 4bit models daily and digitous/ChanSung_Elina_33b-4bit is so far the best for conversations in my experience. What's included. xCytho. Depends on the model. Make sure you only have ONE checkpoint from the two in your model directory! cd alpaca_lora_4bit pip uninstall alpaca_lora_4bit pip uninstall alpaca_lora_4bit # uninstall again to ensure that you do not have another version pip install . Apr 21, 2023 · I was getting CUDA OOM messages after 20 or so replies, so I decided to update GPTQ-for-LLaMa, which seems to be broken now for my Pascal-architecture cards. Quantized 30B is what you can run well on a 3090. 6it/s. 注意事项. 겨우 구동 성공. Llama30B-SuperHOT-4bit-128g. Links to other models can be found in the index at This LoRA is compatible with any 7B, 13B or 30B 4-bit quantized LLaMa model, including ggml quantized converted bins. gpt-x-alpaca had the highest scores on wikitext and PTB_new of the ones I checked. text-generation-inference. Our latest version of Llama is now accessible to individuals, creators, researchers, and businesses of all sizes so that they can experiment, innovate, and scale their ideas responsibly. Console output: C:\Program Files\Python310\lib\site-packages\setuptools\command\ install. Mar 28, 2023 · Describe the bug I am running the new llama-30b-4bit-128g just fine using the latest GPTQ and Webui commits. 54 tokens/s, 1504 tokens, context 33, seed 1719700952)`. It was discovered and developed by kaiokendev. This is the repository for the 7B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Mar 13, 2023 · In this tutorial, you will learn how to run Meta AI's LlaMa 4-bit Model on Google Colab, a free cloud-based platform for running Jupyter notebooks. < llama-30b FP32 2nd load INFO:Loaded the model in 68. Model date LLaMA was trained between December. To download all of them, run: python -m llama. GPT4-X-Alpaca 30B 4-bit working with GPTQ versions used in Oobabooga's Text Generation Webui and KoboldAI. < llama-30b FP16 2nd load INFO:Loaded the model in 39. safetensors in that folder with all associated . 44x more FLOPs. It can be 'jailbroken' easily too. 30B (act-order true-sequential groupsize) wikitext2 4. tsumeone Upload 9 files. Meta Llama 3. • 1 yr. It was trained on more tokens than previous models. like 9. INFO:Loaded the model in 104. 12c7b79 6 months ago. A gaming laptop with RTX3070 and 64GB of RAM costs around $1800, and it could potentially run 16-bit llama 30B with acceptable performance. safetensors. Mar 16, 2023 · qwopqwop200 commented on Apr 1, 2023. Train. How to get oobabooga/text-generation-webui running on Windows or Linux with LLaMa-30b 4bit mode via GPTQ-for-LLaMa on an RTX 3090 start to finish. This was made using Chansung's GPT4-Alpaca Lora. Use build and pip and other standards-based tools. This is possible thanks to novel 4-bit quantization techniques with minimal performance degradation, like GPTQ, GGML, and NF4. We are unlocking the power of large language models. Apr 7, 2023 · I am fine-tuning llama 30b 4-bit with my custom dataset (alpaca_clean + leet10k) then I tried to enable flash attention, I use this command line: python finetune. Perplexity went down a little and I saved about 2. Model: MetaIX/GPT4-X-Alpasta-30b-4bit Env: Intel 13900K, RTX 4090 24GB, DDR5 64GB 4800MHz Performance: 10 tokens/s Reason: This is the best 30B model I've tried so far. I can get up to 1500 tokens returned before it OOMs on 2 x 4090. I happened to do this yesterday, testing the Dromedary 65B 4bit GPTQ I'd just uploaded to HF. You should only use this repository if you have been granted access to the model by filling out this form but either lost your copy of the weights or got some trouble The token rate on the 4bit 30B param model is much faster with llama. In this mode you need to have a model in the memory of each GPU - that's not possible with 30b on 4090 with 24GB VRAM. ) LLaMA-65B 4bit should also work in Colab Pro, but 4bit requires a few more setup steps that are not in my post above. 9 gigs on llama. I've also tested many new 13B models, including Manticore and all the Wizard* models. (1X) RTX 4090 HAGPU Disabled. VRAM usage will depend on the tokens returned. co/tsumeone/llama-30b-supercot-4bit-128g-cuda python llama. In the top left, click the refresh icon next to Model. Apr 6, 2023 · first of all model type should be llama. Recent advancements in weight quantization allow us to run massive large language models on consumer hardware, like a LLaMA-30B model on an RTX 3090 GPU. Instruct-tune LLaMA on consumer hardware. safetensors Evaluation & Score (Lower is better): WikiText2: 4. py install. A 13B model can run on a 12GB GPU and a 30B model can just run on a 24GB GPU (nVidia, really, as CUDA does have an edge over eg. LLaMA-30B-4bit-128g. 'module': model, And then, enabled it and gathered other results. Transformers. py c:\llama-30b-supercot c4 --wbits 4 --true-sequential --groupsize 128 --save_safetensors 4bit-128g. The model will start downloading. cpp. . Mar 12, 2023 · I cant even run 30b-4bit Llama with 24Vram 3090Ti+32GB ram, I can run 13B natively. 5gb Vram just to load for me ,context size will fill up 24gb Vram pretty quick. 112K Members. It will probably work with the 30b and 65b model too, but I haven't tested it. There's a week old bug which causes memory requirements for loading 4bit models to double (or more) which is probably causing your problem. EXLlama. Evaluation & Score (Lower is better): WikiText2: 4. cpp on a M1 Pro than the 4bit model on a 3090 with ooobabooga, and I know it's using the GPU looking at performance monitor on the windows machine. Converted vicuna-13b to GPTQ 4bit using true-sequentual and groupsize 128 in safetensors for best possible model performance. This release includes model weights and starting code for pre-trained and instruction tuned RAM speed is the limiting factor here. 6-7 tokens/s. Inference: Seems slower than GPTQ. 2023. LLaMA-30B trains 32. cpp quantizes to 4-bit, the memory requirements are around 4 times smaller than the original: 7B => ~4 GB; 13B => ~8 GB; 30B => ~16 GB; 64 => ~32 GB; 32gb is probably a little too optimistic, I have DDR4 32gb clocked at 3600mhz and it generates each token every 2 minutes. safetensors " This will work. It was trained in 8bit mode. json files and tokenizer. vicuna-13b-4bit. I think lora fine tuning does not depend a lot on parameter count. kb gr ux st xf li nx vc xu dy