It is a successor to Llama 1, which was released in the first quarter of 2023. Loading the QLORA works, but the speed is pretty lousy so I wanted to either use it with GPTQ or GGML. Or just manually download it. GGML files are for CPU + GPU inference using llama. First, we explore and expand various areas in the same topic using the 7K conversations created by WizardLM. GPTQ quantization is a state of the art quantization method which results in negligible output performance loss when compared with the prior state of the art in 4-bit (. GGML speed strongly depends on the performance and the positioning of RAM slots Reply. Or just manually download it. Tensor library for. llama. q3_K_L. wo, and feed_forward. Another advantage is the. 4bit and 5bit quantised GGML models for CPU inference - TheBloke/stable-vicuna-13B-GGML----- Prompt Template. These aren't the old GGML quants, this was done with the last version before the change to GGUF, and the GGUF is the latest version. This also means you can use much larger model: with 12GB VRAM, 13B is a reasonable limit for GPTQ. Open the text-generation-webui UI as normal. This model has been finetuned from LLama 13B Developed by: Nomic AILarge language models (LLMs) show excellent performance but are compute- and memory-intensive. My understanding was training quantisation was the big breakthrough with qlora, so in terms of comparison it’s apples vs oranges. . 苹果 M 系列芯片，推荐用 llama. GGML vs. e. . Originally, this was the main difference with GPTQ models, which are loaded and run on a GPU. Just anecdotally, switching from a Q4 GPTQ model to Q6_K GGML for MythoMax-L2-13B produced palpable improvements. Please note that these GGMLs are not compatible with llama. bin file is to use this script and this script is keeping the GPTQ quantization, it's not converting it into a q4_1 quantization. cpp team have done a ton of work on 4bit quantisation and their new methods q4_2 and q4_3 now beat 4bit GPTQ in this benchmark. Super fast (12tokens/s) on single GPU. cpp GGML models, so we can compare to figures people have been doing there for a while. Performance: 4 ~ 5 tokens/s. GGUF, previously GGML, is a. Open the text-generation-webui UI as normal. Especially good for story telling. One option to download the model weights and tokenizer of Llama 2 is the Meta AI website. Note that the GPTQ dataset is not the same as the dataset. Immutable fedora won't work, amdgpu-install need /opt access If not using fedora find your distribution's rocm/hip packages and ninja-build for gptq. 0-GPTQ. GGML is a C library for machine learning (ML) — the “GG” refers to the initials of its originator (Georgi Gerganov). We will provide a comprehensive guide on how to implement GPTQ using the AutoGPTQ library. In this blog post, our focus will be on converting models from the HuggingFace format to GGUF. In the Model drop-down: choose the model you just downloaded, stable-vicuna-13B-GPTQ. 2023年8月28日 13:33. However, if your primary concern is efficiency, GPTQ is the optimal choice. This causes various problems. Ah, or are you saying GPTQ is GPU focused unlike GGML in GPT4All, therefore GPTQ is faster in MLC Chat? So my iPhone 13 Mini’s GPU drastically outperforms my desktop’s Ryzen 5 3500? Bingo. Except the gpu version needs auto tuning in triton. GPTQ vs. llama-2-7b. went with 12,12 and that was horrible. 5B parameter Language Model trained on English and 80+ programming languages. Text Generation Transformers English gptj text generation conversational gptq 4bit. And the wildcard is GGML - I wouldn't bet against that becoming the performance champion before long. CPU is generally always 100% on at least one core for gptq inference. New k-quant method. This script duplicates the addend and scale to match ggml's expectations, at the cost of wasting some memory. Download the 3B, 7B, or 13B model from Hugging Face. 1 results in slightly better accuracy. , 2023) was first applied to models ready to deploy. Wait until it says it's finished downloading. GPTQ vs. GGUF, previously GGML, is a quantization method that allows users to use the CPU to run an. I haven't tested perplexity yet, it would be great if someone could do a comparison. < llama-30b FP16 2nd load INFO:Loaded the model in 39. 16 tokens per second (30b), also requiring autotune. Is it faster for inferences than the GPTQ format? You can't compare them because they are for different purposes. From what I've skimmed in their paper, GPTQ uses some tricky linear algebra not only to calculate the weights, but to also store them in some compressed way. Supports NVidia CUDA GPU acceleration. GPTQ. We built Llama-2-7B-32K-Instruct with less than 200 lines of Python script using Together API, and we also make the recipe fully available . With the Q4 GPTQ this is more like 1/3 of the time. GGML, GPTQ, and bitsandbytes all offer unique features and capabilities that cater to different needs. This end up using 3. GPTQ scores well and used to be better than q4_0 GGML, but recently the llama. Pros: GGML was an early attempt to create a file format for storing GPT models. If you mean running time - then that is still pending with int-3 quant and quant 4 with 128 bin size. Note i compared orca-mini-7b vs wizard-vicuna-uncensored-7b (both the q4_1 quantizations) in llama. I've used these with koboldcpp, but CPU-based inference is too slow for regular usage on my laptop. Reply reply more replies. Nomic. privateGPT. 1 results in slightly better accuracy. The benchmark was run on a NVIDIA-A100 instance and the model used was TheBloke/Mistral-7B-v0. If we take any GPTQ model lets say Wizard Vicuna 13B. panchovix. cpp Did a conversion from GPTQ with groupsize 128 to the latest ggml format for llama. By using the GPTQ-quantized version, we can reduce the VRAM requirement from 28 GB to about 10 GB, which allows us to run the Vicuna-13B model on a single consumer GPU. Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. You can find many examples on the Hugging Face Hub, especially from TheBloke . That is, it starts with WizardLM's instruction, and then expands into various areas in one conversation using. ago. 4. the latest version should be 0x67676d66, the old version which needs migration should be: 0x67676d6c. yaml. Now, I've expanded it to support more models and formats. If your cpu (the core that is running python inference) is at 100% and gpu is 25%, the bottleneck is cpu. py does work on the QLORA, but when trying to apply it to a GGML model it refuses and claims it's lacking a dtype. This might help get a 33B model to load on your setup but you can expect shuffling between VRAM and system RAM. cpp you can also consider the following projects: gpt4all - gpt4all: open-source LLM chatbots that you can run anywhere. These files are GGML format model files for Meta's LLaMA 7b. This is possible thanks to novel 4-bit quantization techniques with minimal performance degradation, like GPTQ, GGML, and NF4. GGML - Large Language Models for Everyone: a description of the GGML format provided by the maintainers of the llm Rust crate, which provides Rust bindings for GGML. pip install ctransformers [gptq] Load a GPTQ model using: llm = AutoModelForCausalLM. NF4. For the first time ever, this means GGML can now outperform AutoGPTQ and GPTQ-for-LLaMa inference (though it still loses to exllama) Note: if you test this, be aware that you should now use --threads 1 as it's no longer beneficial to use. GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. cpp with OpenVINO support: . Which version should you use? As a general rule: Use GPTQ if you have a lot of VRAM, use GGML if you have minimal VRAM, and use the base HuggingFace model if you want the original model without any possible negligible intelligence loss from quantization. AWQ, on the other hand, is an activation. This repository contains the code for the ICLR 2023 paper GPTQ: Accurate Post-training Compression for Generative Pretrained Transformers. 2k 3. They appear something like this. You can consider quantization a way to cut down on model size and resource usage, often making the AI slightly dumber. GPTQ model: anon8231489123/vicuna-13b-GPTQ-4bit-128g on huggingfaceoriginal model: lm-. To use with your GPU using GPTQ pick one of the . if you have oobabooga one click install, run cmd_windows. GPTQ is a specific format for GPU only. safetensors: 4: 128: False: 3. In this paper, we address this challenge, and propose GPTQ, a new one-shot weight quantization method based on approximate second-order information, that is both highly-accurate and highly-efficient. Oobabooga: If you require further instruction, see here and hereStep 1: Request download. Next, we will install the web interface that will allow us. Please see below for a list of tools known to work with these model files. Lots of people have asked if I will make 13B, 30B, quantized, and ggml flavors. Oobabooga: If you require further instruction, see here and here Baku. 0. It was discovered and developed by kaiokendev. GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales are quantized with 6 bits. However, on 8Gb you can only fit 7B models, and those are just dumb in comparison to 33B. Quantize your own LLMs using AutoGPTQ. In GPTQ, we apply post-quantization for once, and this results in both memory savings and inference speedup (unlike 4/8-bit quantization which we will go through later). 4bit quantised GPTQ models for GPU inference - TheBloke/stable-vicuna-13B-GPTQ. cpp CPU (+CUDA). GGML vs. Which version should you use? As a general rule: Use GPTQ if you have a lot of VRAM, use GGML if you have. gpt4-x-vicuna-13B-GGML is not uncensored, but. For the first time ever, this means GGML can now outperform AutoGPTQ and GPTQ-for-LLaMa inference (though it still loses to exllama) Note: if you test this, be aware that you should now use --threads 1 as it's no longer beneficial to use. If you are working on a game development project, GGML's specialized features and supportive community may be the best fit. Press the Download button. There's just something unusual/different causing it not to work for you guys as a GPTQ on Windows. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. Build whisper. Python 27. GPTQ (Frantar et al. And I dont think there is literally any faster GPU out there for inference (VRAM Limits excluded) except H100. However, I was curious to see the trade-off in perplexity for the chat. in the download section. 🌙 GGML vs GPTQ vs bitsandbytes Abstract: This article compares GGML, GPTQ, and bitsandbytes in the context of software development. In the Model drop-down: choose the model you just downloaded, vicuna-13B-1. 0-GPTQ. This 13B model was generating around 11tokens/s. It can also be used with LangChain. bin. Once it's finished it will say "Done". ggml for llama. For example, GGML has a couple approaches like "Q4_0", "Q4_1", "Q4_3". Update to include TheBloke_Wizard-Vicuna-13B-Uncensored-GPTQ GPTQ-for-LLaMa VS Auto GPTQ VS ExLlama (This does not change GGML test results. Supports transformers, GPTQ, AWQ, EXL2, llama. Another day, another great model is released! OpenAccess AI Collective's Wizard Mega 13B. Quantize your own LLMs using AutoGPTQ. In order for their Accuracy or perplexity whatever you want to call it. txt","contentType":"file. In the Model drop-down: choose the model you just downloaded, stable-vicuna-13B-GPTQ. safetensors along with all of the . Check the first 4 bytes of the generated file. 3-bit has been shown very unstable ( Dettmers and Zettlemoyer, 2023 ). Setup python and virtual environment. Scales and mins are quantized with 6 bits. 0 to use ex-llama kernels. It became so popular that it has recently been directly integrated into the transformers library. It is a lot smaller and faster to evaluate than. So far, I've run GPTQ and bitsandbytes NF4 on a T4 GPU and found: fLlama-7B (2GB shards) nf4 bitsandbytes quantisation: - PPL: 8. Damp %: A GPTQ parameter that affects how samples are processed for quantisation. cpp library, also created by Georgi Gerganov. Use both exllama and GPTQ. Try 4bit 32G and you will more than likely be happy with the result! When comparing GPTQ-for-LLaMa and llama. ggmlv3. This is probably stupid and maybe ggml already works this way, but I am wondering, since the main bottleneck seems to be memory bandwidth, could the batches be processed in. Learn more about TeamsRunning a 3090 and 2700x, I tried the GPTQ-4bit-32g-actorder_True version of a model (Exllama) and the ggmlv3. 2 toks. 1-GPTQ-4bit-128g. 1-GPTQ-4bit-128g. Click the Model tab. Once it's finished it will say "Done". Model card Files Community. 01 is default, but 0. And I've seen a lot of people claiming much faster GPTQ performance than I get, too. Scales and mins are quantized with 6 bits. You will need auto-gptq>=0. It completely replaced Vicuna for me (which was my go-to since its release), and I prefer it over the Wizard-Vicuna mix (at least until there's an uncensored mix). Tim Dettmers' Guanaco 33B GGML These files are GGML format model files for Tim Dettmers' Guanaco 33B. The original WizardLM, a 7B model, was trained on a dataset of what the creators call evolved instructions. When you run this program you should see output from the trained llama. GPTQ dataset: The dataset used for quantisation. First, we explore and expand various areas in the same topic using the 7K conversations created by WizardLM. Llama 2 Airoboros 7/13/70B GPTQ/GGML Released! Find them on TheBloke's huggingface page! Hopefully, the L2-70b GGML is an 16k edition, with an Airoboros 2. 2023. Compare privateGPT vs GPTQ-for-LLaMa and see what are their differences. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Quantize your own LLMs using AutoGPTQ. Click Download. In practice, GPTQ is mainly used for 4-bit quantization. jsons and . New comments cannot be posted. I didn't end up using the second GPU, but I did need most of the 250GB RAM on that system. . Scales are quantized with 6 bits. cpp. cpp is the slowest, taking 2. AI's original model in float32 HF for GPU inference. cpp just not using the GPU. Note that the 4-element list of dimensions uses 1 as a placeholder for unused dimensions - this is because the product of the dimensions should not equal zero. 0-GPTQ. Block scales and mins are quantized with 4 bits. This is an example to launch koboldcpp in streaming mode, load a 8k SuperHOT variant of a 4 bit quantized ggml model and split it between the GPU and CPU. We’re on a journey to advance and democratize artificial intelligence through open source and open science. In other words, once the model is fully fine-tuned, GPTQ will be applied to reduce its size. GPTQ dataset: The dataset used for quantisation. GPU/GPTQ Usage. cpp is a way to use 4-bit quantization to reduce the memory requirements and speed up the inference. 7 GB, 12. According to open leaderboard on HF, Vicuna 7B 1. Connect and share knowledge within a single location that is structured and easy to search. Specifically, GPTQ can quantize GPT models with 175 billion parameters in approximately four GPU hours, reducing the bitwidth down to 3 or 4. Repositories availableTim Dettmers' Guanaco 65B GGML These files are GGML format model files for Tim Dettmers' Guanaco 65B. Uses GGML_TYPE_Q4_K for the attention. The team is also working on a full benchmark, similar to what was done for GPT4-x-Vicuna. Scales and mins are quantized with 6 bits. This format is good for people that does not have a GPU, or they have a really weak one. This end up using 3. 5B tokens high-quality programming-related data, achieving 73. Repeat the process by entering in the 7B model, TheBloke/WizardLM-7B-V1. To download from a specific branch, enter for example TheBloke/Wizard-Vicuna-30B. 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. Quantized in 8 bit requires 20 GB, 4 bit 10 GB. Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. or. I think the gpu version in gptq-for-llama is just not optimised. Wait until it says it's finished downloading. 5625 bits per weight (bpw)What is gpt4-x-alpaca? gpt4-x-alpaca is a 13B LLaMA model that can follow instructions like answering questions. New comments cannot be posted. For GPTQ I had to have a GPU, so I went back to that 2 x 4090 system @ $1. cpp (GGUF), Llama models. 4 Python text-generation-webui VS GPTQ-for-LLaMa 4 bits quantization of LLaMA using GPTQ InfluxDB. It runs on CPU only. As far as I'm aware, GPTQ 4-bit w/ Exllama is still the best option. GGML is designed for CPU and Apple M series but can also offload some layers on the GPU. GGML files consists of binary-encoded data that is laid out according to a specified. GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. devops","contentType":"directory"},{"name":". Since the original full-precision Llama2 model requires a lot of VRAM or multiple GPUs to load, I have modified my code so that quantized GPTQ and GGML model variants (also known as llama. I tried adjusting the configuration like temperature and other. cuda. EDIT - Just to add, you can also change from 4bit models to 8 bit models. 5 (16k) is fine-tuned from Llama 2 with supervised instruction fine-tuning and linear RoPE scaling. 13B is parameter count, meaning it was trained on 13 billion parameters. But in the end, the models that use this are the 2 AWQ ones and the load_in_4bit one, which did not make it into the VRAM vs perplexity frontier. 0, 0. GPTQ runs on Linux and Windows, usually with NVidia GPU (there is a less-well-supported AMD option as well, possibly Linux only. TheBloke/guanaco-65B-GPTQ. 0. Renamed to KoboldCpp. Big shoutout to The-Bloke who graciously quantized these models in GGML/GPTQ format to further serve the AI community. from_pretrained ("TheBloke/Llama-2-7B-GPTQ") Run in Google Colab. Under Download custom model or LoRA, enter TheBloke/falcon-7B-instruct-GPTQ. Llama-2-7B-32K-Instruct is an open-source, long-context chat model finetuned from Llama-2-7B-32K, over high-quality instruction and chat data. All 3 versions of ggml LLAMA. Train. All reactions. Q&A for work. We will use the 4-bit GPTQ model from this repository. Wait until it says it's finished downloading. GGML — A CPU Optimized Version Big shoutout to The-Bloke who graciously quantized these models in GGML/GPTQ format to further serve the AI community GGML is a C library for machine learning. H2OGPT's OASST1-512 30B GGML These files are GGML format model files for H2OGPT's OASST1-512 30B. GGCC is a new format created in a new fork of llama. The current release includes the following features: An efficient implementation of the GPTQ algorithm: gptq. Teams. Loading: Much slower than GPTQ, not much speed up on 2nd load. 60 GB: 6. Using Llama. Under Download custom model or LoRA, enter TheBloke/stable-vicuna-13B-GPTQ. Wait until it says it's finished downloading. TheBloke/SynthIA-7B-v2. GGML13B Metharme GGML: CPU: Q4_1, Q5_1, Q8: 13B Pygmalion: GPU: Q4 CUDA 128g: 13B Metharme: GPU: Q4 CUDA 128g: VicUnLocked 30B (05/18/2023) A full context LoRA fine-tuned to 1 epoch on the ShareGPT Vicuna Unfiltered dataset, with filtering mostly removed. There's also a half-context 3 epoch version that you can get here. This documents describes the basics of the GGML format, including how quantization is used to democratize access to LLMs. Use llama2-wrapper as your local llama2 backend for Generative Agents/Apps, colab example. License: creativeml-openrail-m. 0 dataset. It can load GGML models and run them on a CPU. 19】:1. cpp (GGUF), Llama models. Some time back I created llamacpp-for-kobold, a lightweight program that combines KoboldAI (a full featured text writing client for autoregressive LLMs) with llama. I've just finished a thorough evaluation (multiple hour-long chats with 274 messages total over both TheBloke/Nous-Hermes-Llama2-GGML (q5_K_M) and TheBloke/Redmond-Puffin-13B-GGML (q5_K_M)) so I'd like to give my feedback. 1-AWQ for. Wait until it says it's finished downloading. The 8bit models are higher quality than 4 bit, but again more memory etc. I think my purpose is not to make it faster but also to experience the different between running GPTQ & GGML modelsVicuna-13b-GPTQ-4bit is amazing. 0. At a higher level, the process involves the following steps: Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. Once you have LLaMA weights in the correct format, you can apply the XOR decoding: python xor_codec. GGML presents an alternative. By reducing the precision ofGGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. In the top left, click the refresh icon next to Model. Testing the new BnB 4-bit or "qlora" vs GPTQ Cuda upvotes. I worked with GPT4 to get it to run a local model, but I am not sure if it hallucinated all of that. When comparing llama. gpt4-x-alpaca’s HuggingFace page states that it is based on the Alpaca 13B model, fine. In the Model dropdown, choose the model you just downloaded: Nous-Hermes-13B-GPTQ. Nevertheless, there is no impediment to running GGUF on a GPU; in fact, it runs even faster compared to CPU execution. Using a dataset more appropriate to the model's training can improve quantisation accuracy. That was it's main purpose, to let the llama. 1 results in slightly better accuracy. The model will start downloading. cppを選ぶメリットが減ってしまう気もする（CPUで動かせる利点は残るものの）。 なお個人の使用実感でいうと、量子化によるテキストの劣化はあまり感じられない。In this blog post, our focus will be on converting models from the HuggingFace format to GGUF. Convert the model to ggml FP16 format using python convert. StarCoderPlus is a fine-tuned version of StarCoderBase on 600B tokens from the English web dataset RedefinedWeb combined with StarCoderData from The Stack (v1. Learning Resources:TheBloke Quantized Models - from Hugging Face (Optimum) -. This ends up effectively using 2. AI's GPT4all-13B-snoozy. Enjoy using the L2-70b variants but don't enjoy the occasional 8 minute wait of a full cublas context refresh lol. A quick glance would reveal that a substantial chunk of these models has been quantified by TheBloke, an influential and respected figure in the LLM community. Step 1. conda activate vicuna. cpp)The response is even better than VicUnlocked-30B-GGML (which I guess is the best 30B model), similar quality to gpt4-x-vicuna-13b but is uncensored. This is the repository for the 7B pretrained model, converted for the Hugging Face Transformers format. 24 seconds. GPTQ-for-LLaMa vs llama. BigCode's StarCoder Plus. So here it is, after exllama, GPTQ and SuperHOT stole GGML the show for a while, finally there's a new koboldcpp version with: full support for GPU acceleration using CUDA and OpenCL. Because of the different quantizations, you can't do an exact comparison on a given seed. 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. Personally I'm more curious into 7900xt vs 4070ti both running GGML models with as many layers on GPU as can fit, the rest on 7950x with 96GB RAM. 5-Mistral-7B-16k-GGUFMPT-7B-Instruct GGML This is GGML format quantised 4-bit, 5-bit and 8-bit GGML models of MosaicML's MPT-7B-Instruct. Click the Refresh icon next to Model in the top left. 30 43,757 7. You may have a different experience. In both cases I'm pushing everything I can to the GPU; with a 4090 and 24gb of ram, that's between 50 and 100 tokens per second (GPTQ has a much more variable. cpp) can. H2OGPT's OASST1-512 30B GGML These files are GGML format model files for H2OGPT's OASST1-512 30B. cpp and libraries and UIs which support this format, such as: text-generation-webui; KoboldCpp; ParisNeo/GPT4All-UI; llama-cpp-python; ctransformers; Repositories available 4-bit GPTQ models for. So, in this article, we will. cpp, which runs the GGML models, added GPU support recently. ago. Specifically, GPTQ can quantize GPT models with 175 billion parameters in approximately four GPU hours, reducing the bitwidth down to 3 or 4 bits. The gpu is waiting for more work while cpu is maxed out. Furthermore, this model is instruction-tuned on the Alpaca/Vicuna format to be steerable and easy-to-use. 8G. --Best--GGML Wizard Vicuna 13B 5_1 GGML Wizard Vicuna 13B 5_0 GPTQ Wizard Vicuna 13B 4bit GGML Wizard Vicuna. Credit goes to TheBloke for creating these models, and kaiokendev for creating SuperHOT (See his blog post here). This end up using 3. . GPTQ uses Integer quantization + an optimization procedure that relies on an input mini-batch to perform the quantization. 01 is default, but 0. As quoted from this site. Falcon 40B-Instruct GGML These files are GGCC format model files for Falcon 40B Instruct. 9. GPTQ-for-LLaMa - 4 bits quantization of LLaMa using GPTQ ggml - Tensor library for machine learning mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices. 4375 bpw. Quantization-Aware Training (QAT) A technique that refines the PTQ model to maintain accuracy even after quantization. GPTQ is post-training quantization method crafted specifically for GPT (Generative Pretrained Transformers) models. Quantized models are available from TheBloke: GGML - GPTQ (You're the best!) Model details The idea behind this merge is that each layer is composed of several tensors, which are in turn responsible for specific functions. 10 GB: New k-quant method. In the top left, click the refresh icon next to Model. GPTQ is a specific format for GPU only. 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. Block scales and mins are quantized with 4 bits. 24 # GPU version!pip install ctransformers[gptq] On you computer: We also outperform a recent Triton implementation for GPTQ by 2. GGUF) Thus far, we have explored sharding and quantization techniques. NF4 vs. 1 GPTQ 4bit runs well and fast, but some GGML models with 13B 4bit/5bit quantization are also good. Hugging Face. 8G.