We dive deep into the world of GPTQ 4-bit quantization for large language models like LLaMa. Performance: 4 ~ 5 tokens/s. 01 is default, but 0. 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. However, llama. This is wizard-vicuna-13b trained with a subset of the dataset - responses that contained alignment / moralizing were removed. < llama-30b-4bit 1st load INFO:Loaded the model in 7. These files are GGML format model files for Meta's LLaMA 7b. This ends up effectively using 2. The GGML_TYPE_Q5_K is a type-1 5-bit quantization, while the GGML_TYPE_Q2_K is a type-1 2-bit quantization. If you mean running time - then that is still pending with int-3 quant and quant 4 with 128 bin size. com. gpt4-x-alpaca’s HuggingFace page states that it is based on the Alpaca 13B model, fine. 4bit and 5bit GGML models for GPU inference. The weights in a GGML file are encoded as a list of layers, the length of which is. GGML: 3 quantized versions. cpp is the slowest, taking 2. Oobabooga: If you require further instruction, see here and hereBaku. I appear to be stuck. Note that the GPTQ dataset is not the same as the dataset. 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. 1 results in slightly better accuracy. 0-Uncensored-GGML or if you have a GPU with 8 GB of VRAM use the GPTQ version instead of the GGML version. conda activate vicuna. Oobabooga: If you require further instruction, see here and here Baku. Reply reply MrTopHatMan90 • Yeah that seems to of worked. cpp (GGUF), Llama models. Type:. Try 4bit 32G and you will more than likely be happy with the result! When comparing GPTQ-for-LLaMa and llama. Under Download custom model or LoRA, enter TheBloke/falcon-7B-instruct-GPTQ. Scales are quantized with 6 bits. Then the new 5bit methods q5_0 and q5_1 are even better than that. It uses the same architecture and is a drop-in replacement for the original LLaMA weights. 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. GPTQ-for-LLaMa. 1. We will try to get in discussions to get the model included in the GPT4All. Gptq-triton runs faster. During GPTQ I saw it using as much as 160GB of RAM. 01 is default, but 0. GPTQ is TERRIBLE with RAM swap, because CPU doesn't compute anything there. github","path":". Maybe now we can do a vs perplexity test to confirm. I'm also still a bit curious of GGML is competitive with GPTQ/exllama when running on Nvidia GPU. q4_0. GPTQ: A Comparative Analysis: While GPT-3’s GPTQ was a significant step in the right direction, GGUF offers several advantages that make it a game-changer: Size and Efficiency: GGUF’s quantization techniques ensure that even the most extensive models are compact without compromising on output quality. It is now able to fully offload all inference to the GPU. cpp. TheBloke/wizardLM-7B-GPTQ. GGML/GGUF models are tailored to minimize memory usage rather than prioritize speed. cpp (GGUF), Llama models. Wait until it says it's finished downloading. We performed some speed, throughput and latency benchmarks using optimum-benchmark library. GPTQ has been very popular to create models in 4-bit precision that can efficiently run on GPUs. 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. Untick Autoload model. Models; Datasets; Spaces; DocsThis video explains difference between GGML and GPTQ in AI models in very easy terms. 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. Reply reply more replies. 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. jsons and . llama. Now click the Refresh icon next to Model in the. You'll need to split the computation between CPU and GPU, and that's an option with GGML. AWQ outperforms round-to-nearest (RTN) and GPTQ across different model scales (7B-65B), task types (common sense vs. For example, from here: TheBloke/Llama-2-7B-Chat-GGML TheBloke/Llama-2-7B-GGML. Step 1. 2023. This might help get a 33B model to load on your setup but you can expect shuffling between VRAM and system RAM. cpp, and also all the newer ggml alpacas on huggingface) GPT-J/JT models (legacy f16 formats here as well as 4 bit quantized ones like this and pygmalion see pyg. 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. 4375 bpw. en-encoder-openvino. GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. By reducing the precision ofGGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. However, there are two differences which I accommodated changing the output format (and adding corresponding support to main. Edit model. mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices. safetensors along with all of the . bin. We've fine-tuned Phind-CodeLlama-34B-v1 on an additional 1. 0-16k-GPTQ:gptq-4bit-32g-actorder_True. 1 results in slightly better accuracy. The model will start downloading. GPTQ simply does less, and once the 4bit inference code is done I. GPT-2 (All versions, including legacy f16, newer format + quanitzed, cerebras) Supports OpenBLAS acceleration only for newer format. Once it's finished it will say "Done". Locked post. So it seems that GPTQ has a similar latency problem. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. 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. You will need auto-gptq>=0. 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 . And the wildcard is GGML - I wouldn't bet against that becoming the performance champion before long. Specifically, GPTQ can quantize GPT models with 175 billion parameters in approximately four GPU hours, reducing the bitwidth down to 3 or 4 bits. In other words, once the model is fully fine-tuned, GPTQ will be applied to reduce its size. We’re on a journey to advance and democratize artificial intelligence through open source and open science. For Kobold CCP you use GGML files insted of the normal gptq or f16 formats. 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. You can consider quantization a way to cut down on model size and resource usage, often making the AI slightly dumber. Next, we will install the web interface that will allow us. Currently, quantizing models are used for two main purposes: So far, two integration efforts have been made and are natively supported in transformers : bitsandbytes and auto-gptq . WizardLM's WizardCoder 15B 1. GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Text Generation Transformers English gptj text generation conversational gptq 4bit. Tim Dettmers' Guanaco 33B GGML These files are GGML format model files for Tim Dettmers' Guanaco 33B. 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. cpp. GGML/GGUF is a C library for machine learning (ML) — the “GG” refers to. marella/ctransformers: Python bindings for GGML models. This is the repository for. Under Download custom model or LoRA, enter TheBloke/stable-vicuna-13B-GPTQ. 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. The model will automatically load, and is now ready for use!GGML vs. GPTQ is a specific format for GPU only. cpp GGML models, so we can compare to figures people have been doing there for a. ) Apparently it's good - very good! Locked post. As this is a GPTQ model, fill in the GPTQ parameters on the right: Bits = 4, Groupsize = 128, model_type = Llama. cpp with all layers offloaded to GPU). I heard that it's slower than GPTQ if GPTQ can run it (meaning it fits into VRAM entirely). jsons and . The paper explains it in more detail, but to summarize, complex instruct means exactly what it sounds like. Quantize your own LLMs using AutoGPTQ. 2) AutoGPTQ claims it doesn't support LORAs. The training data is around 125K conversations collected from ShareGPT. Supports transformers, GPTQ, AWQ, EXL2, llama. When comparing GPTQ-for-LLaMa and llama. 13B is parameter count, meaning it was trained on 13 billion parameters. Quantization can reduce memory and accelerate inference. My CPU is an "old" Threadripper 1950X. jsons and . I am in the middle of some comprehensive GPTQ perplexity analysis - using a method that is 100% comparable to the perplexity scores of llama. . conda activate vicuna. Quantize Llama models with GGML and llama. Just anecdotally, switching from a Q4 GPTQ model to Q6_K GGML for MythoMax-L2-13B produced palpable improvements. Llama, GPTQ 4bit, AutoGPTQ: WizardLM 7B: 43. GGML is designed for CPU and Apple M series but can also offload some layers on the GPU. The GGML format was designed for CPU + GPU inference using llama. GPU/GPTQ Usage. 1. 0-GPTQ. i did the test using theblokes 'TheBloke_guanaco-33B-GGML' vs 'TheBloke_guanaco-33B-GPTQ'. alpaca-lora - Instruct-tune LLaMA on consumer hardware. Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 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. model files. Do you know of any github projects that I could replace GPT4All with that uses CPU-based GPTQ in Python? TheBloke/guanaco-65B-GPTQ. I'll be posting those this weekend. Under Download custom model or LoRA, enter TheBloke/airoboros-33b-gpt4-GPTQ. In practice, GPTQ is mainly used for 4-bit quantization. and some compatibility enhancements. This ends up effectively using 2. There are already bleeding edge 4-bit quantization efforts such as GPTQ for LLaMA. Quantization-Aware Training (QAT) A technique that refines the PTQ model to maintain accuracy even after quantization. --Best--GGML Wizard Vicuna 13B 5_1 GGML Wizard Vicuna 13B 5_0 GPTQ Wizard Vicuna 13B 4bit GGML Wizard Vicuna. 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. For GPTQ I had to have a GPU, so I went back to that 2 x 4090 system @ $1. GPTQ and ggml-q4 both use 4-bit weights, but differ heavily in how they do it. Reply reply MrTopHatMan90 • Yeah that seems to of worked. However, we made it in a continuous conversation format instead of the instruction format. cpp's GGML) that has awesome performance but supports only GPU acceleration. The zeros and. The original WizardLM, a 7B model, was trained on a dataset of what the creators call evolved instructions. Once the quantization is completed, the weights can be stored and reused. 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. After the initial load and first text generation which is extremely slow at ~0. GPTQ dataset: The dataset used for quantisation. from_pretrained ("TheBloke/Llama-2-7b-Chat-GPTQ", torch_dtype=torch. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Renamed to KoboldCpp. How is ggml speed for you vs gptq if you don’t mind me asking? I have a 5800x3d and a 4090 so not too different, but have never tried ggml. devops","path":". domain-specific), and test settings (zero-shot vs. GGML is a C library for machine learning (ML) — the “GG” refers to the initials of its originator (Georgi Gerganov). The lower bit quantization can reduce the file size and memory bandwidth requirements, but also introduce more errors and noise that can affect the accuracy of the model. New comments cannot be posted. Supporting models: Llama-2-7b/13b/70b, Llama-2-GPTQ, Llama-2-GGML, CodeLlama. GPTQ supports amazingly low 3-bit and 4-bit weight quantization. TheBloke/MythoMax-L2-13B-GPTQ VS Other Language Models. Quantized in 8 bit requires 20 GB, 4 bit 10 GB. In the table above, the author also reports on VRAM usage. Once it's finished it will say "Done". No matter what command I used, it still tried to download it. cpp you can also consider the following projects: gpt4all - gpt4all: open-source LLM chatbots that you can run anywhere. A simple one-file way to run various GGML and GGUF models with KoboldAI's UI llama. Downloaded Robin 33B GPTQ and noticed the new model interface, switched over to EXllama and read I needed to put in a split for the cards. 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. py generated the latest version of model. * The inference code needs to know how to "decompress" the GPTQ compression to run inference with them. Supports CLBlast and OpenBLAS acceleration for all versions. 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. The metrics obtained include execution time, memory usage, and. All 3 versions of ggml LLAMA. 9. FP16 (16bit) model required 40 GB of VRAM. wo, and feed_forward. 22x longer than ExLlamav2 to process a 3200 tokens prompt. Low-level APIs are not fully supported. 3-bit has been shown very unstable ( Dettmers and Zettlemoyer, 2023 ). The library is written in C/C++ for efficient inference of Llama models. This end up using 3. text-generation-webui - A Gradio web UI for Large Language Models. Using a dataset more appropriate to the model's training can improve quantisation accuracy. cpp, and adds a versatile Kobold API endpoint, additional format support, backward compatibility, as well as a fancy UI with persistent stories, editing tools, save formats, memory, world info,. If you are working on a game development project, GGML's specialized features and supportive community may be the best fit. This repository contains the code for the ICLR 2023 paper GPTQ: Accurate Post-training Compression for Generative Pretrained Transformers. text-generation-webui - A Gradio web UI for Large Language Models. 4k • 262 lmsys/vicuna-33b-v1. GGML 30B model VS GPTQ 30B model 7900xtx FULL VRAM Scenario 2. 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. The Exllama_HF model loader seems to load GPTQ models. Click Download. In the Download custom model or LoRA text box, enter. In addition to defining low-level machine learning primitives (like a tensor type), GGML defines a binary format for distributing LLMs. Model Architecture Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. Click Download. CUDA ooba GPTQ-for-LlaMa - WizardLM 7B no-act-order. 0-16k-GPTQ:gptq-4bit-32g-actorder_True. Anyone know how to do this, or - even better - a way to LoRA train GGML directly?gptq_model-4bit-128g. According to open leaderboard on HF, Vicuna 7B 1. cpp you can also consider the following projects: gpt4all - gpt4all: open-source LLM chatbots that you can run anywhere. GPTQ clearly outperforms here. Click the Model tab. 01 is default, but 0. cpp is using RTN for 4 bit quantization rather than GPTQ, so I'm not sure if it's directly related. Python 27. The difference for LLaMA 33B is greater than 1 GB. 4bit means how it's quantized/compressed. Inference speed (forward pass only) This. 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 GPU inference其中. Another advantage is the. cpp CPU (+CUDA). LoLLMS Web UI, a great web UI with GPU acceleration via the. I'm also still a bit curious of GGML is competitive with GPTQ/exllama when running on Nvidia GPU. . The team is also working on a full benchmark, similar to what was done for GPT4-x-Vicuna. Unfortunately, while this model does write quite well, it still only takes me about 20 or so messages before it starts showing the same "catch phrase" behavior as the dozen or so other LLaMA 2 models I've tried. Supports transformers, GPTQ, AWQ, EXL2, llama. Under Download custom model or LoRA, enter TheBloke/vicuna-13B-1. Currently these files will also not work with code that. GGML vs. more replies. Finding a way to try GPTQ to. This technique, introduced by Frantar et al. This script duplicates the addend and scale to match ggml's expectations, at the cost of wasting some memory. Specifically, GPTQ can quantize GPT models with 175 billion parameters in approximately four GPU hours, reducing the bitwidth down to 3 or 4. CPP models (ggml, ggmf, ggjt) All versions of ggml ALPACA models (legacy format from alpaca. GGUF is a new format introduced by the llama. github","path":". 01 is default, but 0. Learning Resources:TheBloke Quantized Models - from Hugging Face (Optimum) -. 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. It's the current state-of-the-art amongst open-source models. Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 01 is default, but 0. 55 tokens/s Falcon, unquantised bf16: Eric's base WizardLM-Falcon: 27. 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. Uses GGML_TYPE_Q5_K for the attention. Unique Merging Technique. It loads in maybe 60 seconds. However, that doesn't mean all approaches to quantization are going to be compatible. 3 Python text-generation-webui VS llama Inference code for LLaMA modelsIt still works with Pygmalion 7B GPTQ, but it doesn't seem to work with Wizard Vicuna 13B GGML, although I can load and use the latter in Ooba. In the Model drop-down: choose the model you just downloaded, vicuna-13B-1. empty_cache() everywhere to prevent memory leaks. 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. cpp (GGUF/GGML)とGPTQの2種類が広く使われている。. GGUF / GGML versions run on most computers, mostly thanks to quantization. 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. Furthermore, this model is instruction-tuned on the Alpaca/Vicuna format to be steerable and easy-to-use. 4. Updated the ggml quantizations to be compatible with the latest version of llamacpp (again). 2023年8月28日 13:33. Block scales and mins are quantized with 4 bits. Once it's finished it will say "Done". 1 results in slightly better accuracy. TheBloke/MythoMax-L2-13B-GPTQ differs from other language models in several key ways: 1. For GPTQ I had to have a GPU, so I went back to that 2 x 4090 system @ $1. • 5 mo. NF4Benchmarks. And in my GGML vs GPTQ tests, GGML did 20. 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. Using a dataset more appropriate to the model's training can improve quantisation accuracy. cpp. And it can be applied to LLaMa. Devs playing around with it. Combining Wizard and Vicuna seems to have strengthened the censoring/moralizing stuff each inherited from fine-tuning with Open ClosedAI's ChatGPT even more. In this blog post, our focus will be on converting models from the HuggingFace format to GGUF. Eventually, this gave birth to the GGML format. All reactions. (2) Es ist schwer zu sagen wann man lieber auf ein GPTQ quantisierten oder einen. LLMs are so large it can take a few hours to quantize some these models. I have an Alienware R15 32G DDR5, i9, RTX4090. Q&A for work. GPTQ & GGML allow PostgresML to fit larger models in less RAM. 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. 1. AutoGPTQ is a library that enables GPTQ quantization. Originally, this was the main difference with GPTQ models, which are loaded and run on a GPU. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Uses that GPT doesn’t allow but are legal (for example, NSFW content) Enterprises using it as an alternative to GPT-3. IMO GGML is great (And I totally use it) but it's still not as fast as running the models on GPU for now. Update to include TheBloke_Wizard-Vicuna-13B-Uncensored-GPTQ GPTQ-for-LLaMa VS Auto GPTQ VS ExLlama (This does not change GGML test results. 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). That is, it starts with WizardLM's instruction, and then expands into various areas in one conversation using. Using MythoLogic-L2's robust understanding as its input and Huginn's extensive writing capability as its output seems to. Koala 13B GGML These files are GGML format model files for Koala 13B. Train. In the Model drop-down: choose the model you just downloaded, falcon-40B-instruct-GPTQ. . 0-GPTQ. 45/hour. Damp %: A GPTQ parameter that affects how samples are processed for quantisation. I've recently switched to KoboldCPP + SillyTavern. 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. 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. For more general-purpose projects that require complex data manipulation, GPTQ's flexibility and extensive capabilities. . are other backends with their own quantized format, but they're only useful if you have a recent graphics card (GPU). GGML files are for CPU + GPU inference using llama. safetensors: 4: 128: False: 3. GGML speed strongly depends on the performance and the positioning of RAM slots Reply. I've actually confirmed that this works well in LLaMa 7b. First attempt at full Metal-based LLaMA inference: llama :. In the Model dropdown, choose the model you just downloaded: WizardCoder-Python-34B-V1. Using MythoLogic-L2's robust understanding as its input and Huginn's extensive writing capability as its output seems to. 50 tokens/s, 511 tokens, context 44,. NF4. Quantize Llama models with GGML and llama. For ref, 13900k is 2x the single core performance vs 1950x. GPTQ. TheBloke/MythoMax-L2-13B-GPTQ differs from other language models in several key ways: 1. Click Download. Quantize your own LLMs using AutoGPTQ. 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. AWQ, on the other hand, is an activation-aware weight quantization approach that protects salient weights by. GGUF, previously GGML, is a. 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. GPTQ确实很行，不仅是显存占用角度，精度损失也非常小，运行时间也很短，具体的数值可以看论文里的实验结果，这里就不一一展开来说了。. While Rounding-to-Nearest (RtN) gives us decent int4, one cannot achieve int3 quantization using it. I'm running models in my home pc via Oobabooga. cpp that introduced this new Falcon GGML-based support: cmp-nc/ggllm. Nevertheless, there is no impediment to running GGUF on a GPU; in fact, it runs even faster compared to CPU execution. NF4. . cpp, which runs the GGML models, added GPU support recently. Have ‘char a’ perform an action on ‘char b’ and also have ‘char b’ perform and action on ‘user’ and have ‘user perform an action on either ‘char’ and see how well it keeps up with who is doing. The model will start downloading. GPTQ dataset: The dataset used for quantisation. GPTQ dataset: The dataset used for quantisation. GGML is designed for CPU and Apple M series but can also offload some layers on the GPU. 1 results in slightly better accuracy. However, if your primary concern is efficiency, GPTQ is the optimal choice. Or just manually download it. It's a 15. pt. 0-GPTQ. This user has. Note that the GPTQ dataset is not the same as the dataset. Sol_Ido. Here's some more info on the model, from their model card: Model Description. 60 GB: 6. Disclaimer: The project is coming along, but it's still a work in progress! Hardware requirements. Quantization-Aware Training (QAT) A technique that refines the PTQ model to maintain accuracy even after quantization. Enjoy using the L2-70b variants but don't enjoy the occasional 8 minute wait of a full cublas context refresh lol.