What are the core differences between how GGML, GPTQ and bitsandbytes (NF4) do quantisation? Which will perform best on: a) Mac (I'm guessing ggml) b). The only slowness introduced, as @slaren mentioned, was the removal of the transposed ggml_mul_mat path which led to about %10 performance loss during single-token inference (i. Pick yer size and type! Merged fp16 HF models are also available for 7B, 13B and 65B (33B Tim did himself. cpp. It is now able to fully offload all inference to the GPU. bin IR model files. cpp, which runs the GGML models, added GPU support recently. cpp team on August 21st 2023. 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. cpp) rather than having the script match the existing one: - The tok_embeddings and output weights (i. Llama 2. Probably would want to just call the stuff directly and save the inference test. Supports transformers, GPTQ, AWQ, EXL2, llama. 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. GPU Installation (GPTQ Quantised) First, let’s create a virtual environment: conda create -n vicuna python=3. My 4090 does around 50 t/s at Q4, GPTQ. 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. I've been trying to try different ones, and the speed of GPTQ models are pretty good since they're loaded on GPU, however I'm not sure which one would be the best option for what purpose. Note that the GPTQ dataset is not the same as the dataset. Here's some more info on the model, from their model card: Model Description. Looks like the zeros issue corresponds to a recent commit to GPTQ-for-LLaMa (with a very non-descriptive commit message) which changed the format. Although GPTQ does compression well, its focus on GPU can be a disadvantage if you do not have the hardware to run it. GPTQ is a specific format for GPU only. 苹果 M 系列芯片，推荐用 llama. and that llama. There's just something unusual/different causing it not to work for you guys as a GPTQ on Windows. 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. GGML files are for CPU + GPU inference using llama. GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. GPTQ vs. Step 1. ggml is a tensor library for machine learning to enable large models and high performance on commodity hardware. Transformers / Llama. 1]}. WizardLM's WizardCoder 15B 1. 4bit means how it's quantized/compressed. And I've seen a lot of people claiming much faster GPTQ performance than I get, too. GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. py EvolCodeLlama-7b. Click the Model tab. が、たまに量子化されてい. I didn't end up using the second GPU, but I did need most of the 250GB RAM on that system. devops","path":". Use both exllama and GPTQ. It's the current state-of-the-art amongst open-source models. That being said, given that ggml is now outdated and gguf is the new version I don’t know if that is still the case. For GPTQ tests, I used models with groupsize 128 and no desc_act, which are the ones that are widely used. Download: GGML (Free) Download: GPTQ (Free) Now that you know what iteration of Llama 2 you need,. Uses that GPT doesn’t allow but are legal (for example, NSFW content) Enterprises using it as an alternative to GPT-3. 0-GPTQ. Under Download custom model or LoRA, enter TheBloke/Nous-Hermes-13B-GPTQ. Supporting model backends: tranformers, bitsandbytes(8-bit inference),. Quantization-Aware Training (QAT) A technique that refines the PTQ model to maintain accuracy even after quantization. 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. 5. NF4. The team is also working on a full benchmark, similar to what was done for GPT4-x-Vicuna. GGML vs. In short -- ggml quantisation schemes are performance-oriented, GPTQ tries to minimise quantisation noise. During GPTQ I saw it using as much as 160GB of RAM. Note that the GPTQ dataset is not the same as the dataset. Supports transformers, GPTQ, AWQ, EXL2, llama. 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. 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. Quantize your own LLMs using AutoGPTQ. GGML files are for CPU + GPU inference using llama. Pre-Quantization (GPTQ vs. 8, GPU Mem: 4. 4× since it relies on a high-level language and forgoes opportunities for low-level optimizations. We performed some speed, throughput and latency benchmarks using optimum-benchmark library. CUDA ooba GPTQ-for-LlaMa - WizardLM 7B no-act-order. ) Prompts Various (I'm not actually posting the question/answers it's irreverent for this test as we are checking speeds. GGUF boasts extensibility and future-proofing through enhanced metadata storage. cpp. But with GGML, that would be 33B. This 13B model was generating around 11tokens/s. Only the GPTQ models. This technique, introduced by Frantar et al. 1 results in slightly better accuracy. Wait until it says it's finished downloading. Scales and mins are quantized with 6 bits. I am in the middle of some comprehensive GPTQ perplexity analysis - using a method that is 100% comparable to the perplexity scores of llama. 57 (4 threads, 60 layers offloaded) on a 4090, GPTQ is significantly faster. Subreddit to discuss about Llama, the large language model created by Meta AI. Note that the GPTQ dataset is not the same as the dataset. Model: TheBloke/Wizard-Vicuna-7B-Uncensored-GGML. GGML, GPTQ, and bitsandbytes all offer unique features and capabilities that cater to different needs. domain-specific), and test settings (zero-shot vs. There are already bleeding edge 4-bit quantization efforts such as GPTQ for LLaMA. The model will start downloading. On my box with Intel 13900K CPU, the 4090 is running at 100%. GPTQ. Under Download custom model or LoRA, enter this repo name: TheBloke/stable-vicuna-13B-GPTQ. You can find many examples on the Hugging Face Hub, especially from TheBloke . Others are having issues with llama. If you mean running time - then that is still pending with int-3 quant and quant 4 with 128 bin size. In the top left, click the refresh icon next to Model. Last week, Hugging Face announced that Transformers and TRL now natively support AutoGPTQ. 24 # GPU version!pip install ctransformers[gptq] On you computer: We also outperform a recent Triton implementation for GPTQ by 2. A general sentiment I’ve gotten from the community is that ggml vs gptq is akin to accuracy vs speed. cpp. The original WizardLM, a 7B model, was trained on a dataset of what the creators call evolved instructions. There are 2 main formats for quantized models: GGML (now called GGUF) and GPTQ. Pygmalion 7B SuperHOT 8K GPTQ. This repository contains the code for the ICLR 2023 paper GPTQ: Accurate Post-training Compression for Generative Pretrained Transformers. For more general-purpose projects that require complex data manipulation, GPTQ's flexibility and extensive capabilities. Repositories available 4bit GPTQ models for GPU inference. Under Download custom model or LoRA, enter TheBloke/Wizard-Vicuna-13B-Uncensored-SuperHOT-8K-GPTQ. py Compressing all models from the OPT and BLOOM families to 2/3/4 bits, including. wv, attention. This is possible thanks to novel 4-bit quantization techniques with minimal performance degradation, like GPTQ, GGML, and NF4. GPTQ tries to solve an optimization problem for each. txt","path":"examples/whisper/CMakeLists. These files are GGML format model files for Eric Hartford's Wizard Vicuna 13B Uncensored. Model: TheBloke/Wizard-Vicuna-7B-Uncensored-GGML. 1-GPTQ-4bit-128g. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":". Lots of people have asked if I will make 13B, 30B, quantized, and ggml flavors. GPTQ vs. Share Sort by: Best. GPTQ is better, when you can fit your whole model into memory. As GGML models with the same amount of parameters are way smaller than PyTorch models, do GGML models have less quality? Thanks! comments sorted by Best Top New Controversial Q&A Add a Comment More posts you may like. This is the repository for. model files. Wizard Mega 13B GGML This is GGML format quantised 4bit and 5bit models of OpenAccess AI Collective's Wizard Mega 13B. GPTQ means it will run on your graphics card at 4bit (vs GGML which runs on CPU, or the non-GPTQ version which runs at 8bit). 4-bit, 5-bit 8-bit GGML models for llama. GPTQ has been very popular to create models in 4-bit precision that can efficiently run on GPUs. I've used these with koboldcpp, but CPU-based inference is too slow for regular usage on my laptop. AWQ outperforms round-to-nearest (RTN) and GPTQ across different model scales (7B-65B), task types (common sense vs. GGML vs GPTQ — Source:1littlecoder 2. Updated to the latest fine-tune by Open Assistant oasst-sft-7-llama-30b-xor. Moving on to speeds: EXL2 is the fastest, followed by GPTQ through ExLlama v1. 2 toks. 24 seconds. Llama, GPTQ 4bit, AutoGPTQ: WizardLM 7B: 43. cpp. GPTQ model: anon8231489123/vicuna-13b-GPTQ-4bit-128g on huggingfaceoriginal model: lm-. 13B is parameter count, meaning it was trained on 13 billion parameters. GGUF is a new format. In addition to defining low-level machine learning primitives (like a tensor type), GGML defines a binary format for distributing LLMs. • 5 mo. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Damp %: A GPTQ parameter that affects how samples are processed for quantisation. gpt4-x-alpaca’s HuggingFace page states that it is based on the Alpaca 13B model, fine. cpp (GGUF/GGML)とGPTQの2種類が広く使われている。. 主要なモデルは TheBloke 氏によって迅速に量子化されるので、基本的に自分で量子化の作業をする必要はない。. Edit model. So the first step are always to install the dependencies: On Google Colab: # CPU version!pip install ctransformers>=0. In addition to defining low-level machine learning primitives (like a tensor type), GGML defines a binary format for distributing LLMs. My machine has 8 cores and 16 threads so I'll be. Click the Refresh icon next to Model in the top left. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. marella/ctransformers: Python bindings for GGML models. In practice, GPTQ is mainly used for 4-bit quantization. As a general rule of thumb, if you're using an NVIDIA GPU and your entire model will fit in VRAM, GPTQ will be the fastest for you. Note: Download takes a while due to the size, which is 6. GPTQ uses Integer quantization + an optimization procedure that relies on an input mini-batch to perform the quantization. 其实有一个感想是目前. GGML vs. llama2-wrapper. . nf4 without double quantization significantly uses more memory than GPTQ. Damp %: A GPTQ parameter that affects how samples are processed for quantisation. It's the reason there's no GGML k-quants for Open Llama 3B yet, and it also causes this GPTQ issue. Is this a realistic comparison? In that case, congratulations! GGML was designed to be used in conjunction with the llama. in the download section. 4k • 262 lmsys/vicuna-33b-v1. Model Description. You'd have the best luck with NVIDIA GPUs, but with AMD GPUs, your mileage may vary. Nevertheless, there is no impediment to running GGUF on a GPU; in fact, it runs even faster compared to CPU execution. 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. jsons and . I understand your suggestion (=), using a higher bit ggml permuation of the model. That is, it starts with WizardLM's instruction, and then expands into various areas in one conversation using. Specifically, GPTQ can quantize GPT models with 175 billion parameters in approximately four GPU hours, reducing the bitwidth down to 3 or 4. Once it's finished it will say "Done". I am on the razer edge, but I was able to have an 8 hour RP with that of around 868K Tokens sent total for the entire session. Learn how to use PostgresML to fit larger models in less RAM by quantizing them with GPTQ or GGML, two open source libraries that reduce the model size in. 4. 60 GB: 6. cpp / GGUF / GGML / GPTQ & other animals. GGML is a C library for machine learning (ML) — the “GG” refers to the initials of its originator (Georgi Gerganov). GPTQ simply does less, and once the 4bit inference code is done I. KoboldAI (Occam's) + TavernUI/SillyTavernUI is pretty good IMO. Are we just kidding ourselves and it's more the randomness as to what you get. NF4 vs. Supports transformers, GPTQ, AWQ, EXL2, llama. Another day, another great model is released! OpenAccess AI Collective's Wizard Mega 13B. AI's original model in float32 HF for GPU inference. It runs on CPU only. I plan to make 13B and 30B, but I don't have plans to make quantized models and ggml, so I will rely on the community for that. cpp/GGML CPU inference, which enables lower cost hosting vs the standard pytorch/transformers-based GPU hosting. 0. Once the quantization is completed, the weights can be stored and reused. Links to other models can be found in the index at the bottom. Model Description. B GGML 30B model 50-50 RAM/VRAM split vs GGML 100% VRAM In general, for GGML models , is there a ratio of VRAM/ RAM. GGML files are for CPU + GPU inference using llama. Block scales and mins are quantized with 4 bits. GPU/GPTQ Usage. 0 model and it seems it was trained on the following template: ### Human: <your prompt here> ### Assistant:With this option you use the GGML format model and LLaMA interface called llama. Scales are quantized with 6 bits. In the Model drop-down: choose the model you just downloaded, falcon-40B-instruct-GPTQ. What's especially cool about this release is that Wing Lian has prepared a Hugging Face space that provides access to the model using llama. However, on 8Gb you can only fit 7B models, and those are just dumb in comparison to 33B. cpp (GGUF), Llama models. Originally, this was the main difference with GPTQ models, which are loaded and run on a GPU. Context sizes: (512 | 1024 | 2048) ⨯ (7B | 13B | 30B | 65B) ⨯ (llama | alpaca[-lora] | vicuna-GPTQ) models, first 406 lines of wiki. For example, GGML has a couple approaches like "Q4_0", "Q4_1", "Q4_3". If your cpu (the core that is running python inference) is at 100% and gpu is 25%, the bottleneck is cpu. , only utilizes 4 bits and represents a significant advancement in the field of weight quantization. GGUF / GGML versions run on most computers, mostly thanks to quantization. Click the Model tab. Tim Dettmers' Guanaco 33B GGML These files are GGML format model files for Tim Dettmers' Guanaco 33B. Please see below for a list of tools known to work with these model files. Click Download. 1 results in slightly better accuracy. AI's original model in float32 HF for GPU inference. Note that the GPTQ dataset is not the same as the dataset. However, if your primary concern is efficiency, GPTQ is the optimal choice. Gptq-triton runs faster. I tried adjusting the configuration like temperature and other. Train. Low-level APIs are not fully supported. I heard that it's slower than GPTQ if GPTQ can run it (meaning it fits into VRAM entirely). float16, device_map="auto"). 01 is default, but 0. wo, and feed_forward. . 01 is default, but 0. devops","contentType":"directory"},{"name":". Their rate of progress is incredible. This repo is the result of quantising to 4bit and 5bit GGML for CPU inference using llama. Format . Please specify it manually using --model_type argument Press any key to continue . First, we explore and expand various areas in the same topic using the 7K conversations created by WizardLM. 4bit quantised GPTQ models for GPU inference - TheBloke/stable-vicuna-13B-GPTQ. Text Generation • Updated Sep 27 • 23. The default templates are a bit special, though. Click the Refresh icon next to Model in the top left. 开箱即用，选择 gpt4all，有桌面端软件。. Click Download. The GGML format was designed for CPU + GPU inference using llama. In the Model drop-down: choose the model you just downloaded, falcon-7B. Stars - the number of stars that a project has. Click Download. 2t/s, suhsequent text generation is about 1. It is a lot smaller and faster to evaluate than. Bitsandbytes can perform integer quantization but also supports many other formats. cpp (a lightweight and fast solution to running 4bit quantized llama models locally). This llama 2 model is an improved version of MythoMix, which is a merge of MythoLogic-L2 and Huginn using a highly experimental tensor-type merge technique. safetensors along with all of the . #ggml #gptq PLEASE FOLLOW ME: LinkedIn: to unquantized models, this method uses almost 3 times less VRAM while providing a similar level of accuracy and faster generation. cpp just not using the GPU. I'm also still a bit curious of GGML is competitive with GPTQ/exllama when running on Nvidia GPU. They collaborated with LAION and Ontocord to create the training dataset. the latest version should be 0x67676d66, the old version which needs migration should be: 0x67676d6c. kimono-v1-13b-llama2-chat. NF4. 29. GGML: 3 quantized versions. These are SuperHOT GGMLs with an increased context length. cpp. 1 GPTQ 4bit 128g loads ten times longer and after that generate random strings of letters or do nothing. More for CPU muggles (/s) or more for Nvidia wizards? Primarily CPU because it's based on GGML, but ofc it can do GPU offloading Does it implies having the usual impossible-to-get-right settings somehow a bit more self-managed$ . I'm working on more tests with other models and I'll post those when its. text-generation-webui - A Gradio web UI for Large Language Models. GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. GPTQ dataset: The dataset used for quantisation. OpenLLaMA is an openly licensed reproduction of Meta's original LLaMA model. GGCC is a new format created in a new fork of llama. We'll explore the mathematics behind quantization, immersion fea. safetensors: 4: 128: False: 3. In other words, once the model is fully fine-tuned, GPTQ will be applied to reduce its size. Super fast (12tokens/s) on single GPU. artoonu. Click Download. 1. Tensor library for. Text Generation Transformers English gptj text generation conversational gptq 4bit. Right, those are GPTQ for GPU versions. llama. In the top left, click the refresh icon next to. 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. The model will automatically load, and is now ready for use!GGML vs. In the table above, the author also reports on VRAM usage. We will try to get in discussions to get the model included in the GPT4All. 3-bit has been shown very unstable ( Dettmers and Zettlemoyer, 2023 ). As illustrated in Figure 1, relative to prior work, GPTQ is the first method to reliably compress LLMs to 4 bits or less, more than doubling compression at minimal accuracy loss, and allowing for the first time to fit an OPT-175B modelGGUF vs. This might help get a 33B model to load on your setup but you can expect shuffling between VRAM and system RAM. ML Blog - 4-bit LLM Quantization with GPTQI think it's still useful - GPTQ or straight 8-bit quantization in Transformers are tried and tested, and new methods might be buggier. GGML is a C library for machine learning (ML) — the “GG” refers to the initials of its originator (Georgi Gerganov). GPTQ is better, when you can fit your whole model into memory. 4375 bpw. cpp. At a higher level, the process involves. Env: Mac M1 2020, 16GB RAM. 4bit means how it's quantized/compressed. BigCode's StarCoder Plus. INFO:Loaded the model in 104. In order for their Accuracy or perplexity whatever you want to call it. This ends up effectively using 2. I have suffered a lot with out of memory errors and trying to stuff torch. bitsandbytes: VRAM Usage. Another advantage is the. Oobabooga's got bloated and recent updates throw errors with my 7B-4bit GPTQ getting out of memory. 0. 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. Scales are quantized with 6 bits. --Best--GGML Wizard Vicuna 13B 5_1 GGML Wizard Vicuna 13B 5_0 GPTQ Wizard Vicuna 13B 4bit GGML Wizard Vicuna. GPTQ dataset: The dataset used for quantisation. 0, 0. 4375 bpw. ggml is a library that provides operations for running machine learning models. Convert the model to ggml FP16 format using python convert. < llama-30b-4bit 2nd. Quantization can reduce memory and accelerate inference. ExLlamaV2 is a library designed to squeeze even more performance out of GPTQ. GPT-2 (All versions, including legacy f16, newer format + quanitzed, cerebras) Supports OpenBLAS acceleration only for newer format. GPU Installation (GPTQ Quantised) First, let’s create a virtual environment: conda create -n vicuna python=3. It has \"levels\" that range from \"q2\" (lightest, worst quality) to \"q8\" (heaviest, best quality). Unique Merging Technique. from_pretrained ("TheBloke/Llama-2-7b-Chat-GPTQ", torch_dtype=torch. I noticed SSD activities (likely due to low system RAM) on the first text generation. 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. 主要なモデルは TheBloke 氏によって迅速に量子化されるので、基本的に自分で量子化の作業をする必要はない。. . Devs playing around with it. 1 results in slightly better accuracy. 5-16K-GPTQ via AutoGPTQ which should theoretically give me same results as the same model of GGUF type but with even better speeds. 0. GPTQ, AWQ, and GGUF are all methods for weight quantization in large language models (LLMs). 1 results in slightly better accuracy. I've used these with koboldcpp, but CPU-based inference is too slow for regular usage on my laptop. In addition to defining low-level machine learning primitives (like a tensor. 0 to use ex-llama kernels. cpp and GPTQ-for-LLaMa you can also consider the following projects: gpt4all - gpt4all: open-source LLM chatbots that you can run anywhere. Updated to the latest fine-tune by Open Assistant oasst-sft-7-llama-30b-xor. i understand that GGML is a file format for saving model parameters in a single file, that its an old problematic format, and GGUF is the new kid on the block, and GPTQ is the same. This format is good for people that does not have a GPU, or they have a really weak one. KoboldCpp is an easy-to-use AI text-generation software for GGML and GGUF models. GGML is a C library for machine learning. (2) Es ist schwer zu sagen wann man lieber auf ein GPTQ quantisierten oder einen. Learning Resources:TheBloke Quantized Models - from Hugging Face (Optimum) -. GGML, GPTQ, and bitsandbytes all offer unique features and capabilities that cater to different needs. Tested both with my usual setup (koboldcpp, SillyTavern, and simple-proxy-for-tavern - I've posted more details about it in. 0. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":". 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. Model Architecture Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. Finally, and unrelated to the GGML, I then made GPTQ 4bit quantisations. Inference speed (forward pass only) This. Click the Refresh icon next to Model in the top left. You will need auto-gptq>=0. Along with most 13B models ran in 4bit with around Pre-layers set to 40 in Oobabooga. model files. 4 Python text-generation-webui VS GPTQ-for-LLaMa 4 bits quantization of LLaMA using GPTQ InfluxDB. Locked post. 58 seconds. In the Model drop-down: choose the model you just downloaded, stable-vicuna-13B-GPTQ. gptq_model-4bit-128g. ローカルLLMの量子化フォーマットとしては、llama. #ggml #gptq PLEASE FOLLOW ME: LinkedIn: number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives. Further, we show that our model can also provide robust results in the extreme quantization regime,WizardLM-7B-uncensored-GGML is the uncensored version of a 7B model with 13B-like quality, according to benchmarks and my own findings. But that was not the case unfortunately. GGUF and GGML are file formats used for storing models for inference, particularly in the context of language models like GPT (Generative Pre-trained Transformer). Scales and mins are quantized with 6 bits. Here are the ggml versions: The unfiltered vicuna-AlekseyKorshuk-7B-GPTQ-4bit-128g-GGML and the newer vicuna-7B-1. The speed was ok on both (13b) and the quality was much better on the "6. But Vicuna 13B 1. Supports transformers, GPTQ, AWQ, EXL2, llama. Just monitor your cpu usage vs gpu usage. 44 tokens/sClick the Model tab. These files will not work in llama. Repositories available 4-bit GPTQ models for GPU inference. When comparing GPTQ-for-LLaMa and llama. Press the Download button. In the top left, click the refresh icon next to Model. q4_0. Models by stock have 16bit precision, and each time you go lower, (8 bit, 4bit, etc) you sacrifice some. Nomic. Using a dataset more appropriate to the model's training can improve quantisation accuracy. cpp team on August 21st 2023. Updated the ggml quantizations to be compatible with the latest version of llamacpp (again). 1 results in slightly better accuracy. cpp supports it, but ooba does not. It comes under an Apache-2.