H2OGPT's OASST1-512 30B GGML These files are GGML format model files for H2OGPT's OASST1-512 30B. Ok_Ready_Set_Go. 2023年8月28日 13:33. 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. I'm stuck with ggml's with my 8GB vram vs 64 GB ram. Click the Refresh icon next to Model in the top left. 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. GGML/GGUF models are tailored to minimize memory usage rather than prioritize speed. Output Models generate text only. Next, we will install the web interface that will allow us. Pick yer size and type! Merged fp16 HF models are also available for 7B, 13B and 65B (33B Tim did himself. Another day, another great model is released! OpenAccess AI Collective's Wizard Mega 13B. H2OGPT's OASST1-512 30B GGML These files are GGML format model files for H2OGPT's OASST1-512 30B. Click Download. py EvolCodeLlama-7b. 24 # GPU version!pip install ctransformers[gptq] On you computer: We also outperform a recent Triton implementation for GPTQ by 2. Or just manually download it. The metrics obtained include execution time, memory usage, and. Updated to the latest fine-tune by Open Assistant oasst-sft-7-llama-30b-xor. 01 is default, but 0. Renamed to KoboldCpp. Repositories available 4bit GPTQ models for GPU inference. GPTQ vs. Click Download. And I've seen a lot of people claiming much faster GPTQ performance than I get, too. GPTQ is better, when you can fit your whole model into memory. GPTQ is a specific format for GPU only. Oobabooga: If you require further instruction, see here and here Baku. This adds full GPU acceleration to llama. 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. Note that the GPTQ dataset is not the same as the dataset. float16, device_map="auto") Check out the Transformers documentation to. There are 2 main formats for quantized models: GGML (now called GGUF) and GPTQ. They appear something like this. Click the Model tab. GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. This end up using 3. Click Download. One option to download the model weights and tokenizer of Llama 2 is the Meta AI website. Click the Model tab. 0 license, with full access to source code, model weights, and training datasets. I'm working on more tests with other models and I'll post those when its. 4. I don't usually use ggml as it's slower than gptq models by a factor of 2x using GPU. Instead, these models have often already been sharded and quantized for us to use. cpp supports it, but ooba does not. domain-specific), and test settings (zero-shot vs. The GGML format was designed for CPU + GPU inference using llama. The model will automatically load, and is now. Scales and mins are quantized with 6 bits. Currently 4-bit (RtN) with 32 bin-size is supported by GGML implementations. the latest version should be 0x67676d66, the old version which needs migration should be: 0x67676d6c. cpp (GGUF/GGML)とGPTQの2種類が広く使われている。. Note that the GPTQ dataset is not the same as the dataset. GGML makes use of a technique called \"quantization\" that allows for large language models to run on consumer hardware. GPU/GPTQ Usage. Tim Dettmers' Guanaco 33B GGML These files are GGML format model files for Tim Dettmers' Guanaco 33B. This ends up effectively using 2. NousResearch's Nous-Hermes-13B GPTQ. Update to include TheBloke_Wizard-Vicuna-13B-Uncensored-GPTQ GPTQ-for-LLaMa VS Auto GPTQ VS ExLlama (This does not change GGML test results. Once it's finished it will say "Done". GPTQ versions, GGML versions, HF/base versions. In addition to defining low-level machine learning primitives (like a tensor type), GGML defines a binary format for distributing LLMs. en-encoder-openvino. Big shoutout to The-Bloke who graciously quantized these models in GGML/GPTQ format to further serve the AI community. Supports transformers, GPTQ, AWQ, EXL2, llama. 3TheBloke/Wizard-Vicuna-30B-Uncensored-GPTQ. Discord For further support, and discussions on these models and AI in general, join us at:ただ、それだとGPTQによる量子化モデル(4-bit)とサイズが変わらないので、llama. Training Details. Input Models input text only. Reply reply MrTopHatMan90 • Yeah that seems to of worked. So it seems that GPTQ has a similar latency problem. Results. Is this a realistic comparison? In that case, congratulations! GGML was designed to be used in conjunction with the llama. Launch text-generation-webui. 9 min read. GPTQ is better, when you can fit your whole model into memory. Another advantage is the. /bin/gpt-2 [options] options: -h, --help show this help message and exit -s SEED, --seed SEED RNG seed (default: -1) -t N, --threads N number of threads to use during computation (default: 8) -p PROMPT, --prompt PROMPT prompt to start generation with (default: random) -n N, --n_predict N number of tokens to predict. Click Download. 8k • 427 TheBloke/OpenHermes-2. py Compressing all models from the OPT and BLOOM families to 2/3/4 bits, including. GPTQ supports amazingly low 3-bit and 4-bit weight quantization. Anyone know how to do this, or - even better - a way to LoRA train GGML directly?gptq_model-4bit-128g. py generated the latest version of model. 1-GPTQ-4bit-128g. Once it's finished it will say "Done". The training data is around 125K conversations collected from ShareGPT. I noticed SSD activities (likely due to low system RAM) on the first text generation. 45/hour. Python 27. TheBloke/SynthIA-7B-v2. Oobabooga’s Text Generation WebUI [15]: A very versatile Web UI for running LLMs, compatible with both GPTQ and GGML models with many configuration options. GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. cpp, text-generation-webui or KoboldCpp. My understanding was training quantisation was the big breakthrough with qlora, so in terms of comparison it’s apples vs oranges. Update 1: added a mention to. In the top left, click the refresh icon next to Model. {"payload":{"allShortcutsEnabled":false,"fileTree":{"examples/whisper":{"items":[{"name":"CMakeLists. are other backends with their own quantized format, but they're only useful if you have a recent graphics card (GPU). Using a dataset more appropriate to the model's training can improve quantisation accuracy. mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices. This documents describes the basics of the GGML format, including how quantization is used to democratize access to LLMs. #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. 4. Note at that time of writing this documentation section, the available quantization methods were: awq, gptq and bitsandbytes. Scales are quantized with 6 bits. 1. It runs on CPU only. But for me, using Oobabooga branch of GPTQ-for-LLaMA AutoGPTQ versus llama-cpp-python 0. if you have oobabooga one click install, run cmd_windows. It's recommended to relocate these to the same folder as ggml models, as that is the default location that the OpenVINO extension will search at runtime. Note that the GPTQ dataset is not the same as the dataset. cpp, or currently with text-generation-webui. Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. However, that doesn't mean all approaches to quantization are going to be compatible. Damp %: A GPTQ parameter that affects how samples are processed for quantisation. cpp (a lightweight and fast solution to running 4bit quantized llama models locally). 4375 bpw. Under Download custom model or LoRA, enter TheBloke/Wizard-Vicuna-13B-Uncensored-SuperHOT-8K-GPTQ. However, llama. . 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. 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. cpp. GGUF boasts extensibility and future-proofing through enhanced metadata storage. 2023. GPU/GPTQ Usage. GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. So the first step are always to install the dependencies: On Google Colab: # CPU version!pip install ctransformers>=0. 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. Pygmalion 7B SuperHOT 8K GGML. Finally, and unrelated to the GGML, I then made GPTQ 4bit quantisations. 01 is default, but 0. devops","contentType":"directory"},{"name":". 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. GPTQ scores well and used to be better than q4_0 GGML, but recently the llama. I am in the middle of some comprehensive GPTQ perplexity analysis - using a method that is 100% comparable to the perplexity scores of llama. 0. 3. , only utilizes 4 bits and represents a significant advancement in the field of weight quantization. Text Generation Transformers English gptj text generation conversational gptq 4bit. Lots of people have asked if I will make 13B, 30B, quantized, and ggml flavors. It was discovered and developed by kaiokendev. To recap, every Spark. This might help get a 33B model to load on your setup but you can expect shuffling between VRAM and system RAM. Disclaimer: The project is coming along, but it's still a work in progress! Hardware requirements. AWQ vs. When comparing llama. Can ' t determine model type from model name. We performed some speed, throughput and latency benchmarks using optimum-benchmark library. No matter what command I used, it still tried to download it. That's like 50% of the whole job. That's it. Loading ggml-vicuna-13b. GPTQ dataset: The dataset used for quantisation. 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. GPTQ dataset: The dataset used for quantisation. cpp (GGUF), Llama models. GPTQ simply does less, and once the 4bit inference code is done I. model-specific. The 8bit models are higher quality than 4 bit, but again more memory etc. Damp %: A GPTQ parameter that affects how samples are processed for quantisation. This causes various problems. < llama-30b-4bit 1st load INFO:Loaded the model in 7. We will use the 4-bit GPTQ model from this repository. 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. TheBloke/wizardLM-7B-GPTQ. Tested both with my usual setup (koboldcpp, SillyTavern, and simple-proxy-for-tavern - I've posted more details about it in. But with GGML, that would be 33B. GGML: 3 quantized versions. In the Model drop-down: choose the model you just downloaded, falcon-40B-instruct-GPTQ. I've used these with koboldcpp, but CPU-based inference is too slow for regular usage on my laptop. . TheBloke/guanaco-65B-GGML. 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. 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. 除了目前已有的4bit，3bit的量化，论文里在结尾还暗示了2bit量化的可能性，真的令人兴奋。. This model has been finetuned from LLama 13B Developed by: Nomic AILarge language models (LLMs) show excellent performance but are compute- and memory-intensive. w2 tensors, GGML_TYPE_Q2_K for the other tensors. And in my GGML vs GPTQ tests, GGML did 20. 4-bit, 5-bit 8-bit GGML models for llama. jsons and . gpt4-x-alpaca’s HuggingFace page states that it is based on the Alpaca 13B model, fine. ggmlv3. conda activate vicuna. privateGPT. Under Download custom model or LoRA, enter TheBloke/Nous-Hermes-13B-GPTQ. Open the text-generation-webui UI as normal. * The inference code needs to know how to "decompress" the GPTQ compression to run inference with them. GGML vs GPTQ — Source:1littlecoder 2. 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. Note that the GPTQ dataset is not the same as the dataset. You may have a different experience. 0 to use ex-llama kernels. Half precision floating point, and quantization optimizations are now available for your favorite LLMs downloaded from Huggingface. GPTQ dataset: The dataset used for quantisation. bin: q3_K_L: 3: 3. So far, I've run GPTQ and bitsandbytes NF4 on a T4 GPU and found: fLlama-7B (2GB shards) nf4 bitsandbytes quantisation: - PPL: 8. Note that the GPTQ dataset is not the same as the dataset. Finding a way to try GPTQ to compareIt is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. First attempt at full Metal-based LLaMA inference: llama :. 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. Wait until it says it's finished downloading. GGUF / GGML versions run on most computers, mostly thanks to quantization. GPTQ & GGML allow PostgresML to fit larger models in less RAM. Click Download. so thank you so much for taking the time to post this. 01 is default, but 0. People on older HW still stuck I think. One quantized using q4_1, another one was quantized using q5_0, and the last one was quantized using q5_1. 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. 65 seconds (4. You couldn't load a model that had its tensors quantized with GPTQ 4bit into an application that expected GGML Q4_2 quantization and vice versa. The intent is to train a WizardLM that doesn't have alignment built-in, so that alignment (of any sort) can be added separately with for example with a. GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. 4k • 262 lmsys/vicuna-33b-v1. GPTQ is currently the SOTA one shot quantization method for LLMs. auto-gptq: 4-bit quantization with exllama kernels. By reducing the precision of their. 4bit GPTQ models for GPU inference. Tensor library for. Gptq-triton runs faster. , 2023) was first applied to models ready to deploy. TheBloke/MythoMax-L2-13B-GPTQ differs from other language models in several key ways: 1. cpp. KoboldAI (Occam's) + TavernUI/SillyTavernUI is pretty good IMO. In the Model dropdown, choose the model you just downloaded: WizardCoder-15B-1. A general sentiment I’ve gotten from the community is that ggml vs gptq is akin to accuracy vs speed. GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. I haven't tested perplexity yet, it would be great if someone could do a comparison. Share Sort by: Best. 4bit means how it's quantized/compressed. These files are GGML format model files for Meta's LLaMA 7b. It needs to run on a GPU. 1-GPTQ-4bit-128g-GGML. 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. Specifically, GPTQ can quantize GPT models with 175 billion parameters in approximately four GPU hours, reducing the bitwidth down to 3 or 4. 4375 bpw. We can see that nf4-double_quant and GPTQ use almost the same amount of memory. I am in the middle of some comprehensive GPTQ perplexity analysis - using a method that is 100% comparable to the perplexity scores of llama. Scales are quantized with 6 bits. If you are working on a game development project, GGML's specialized features and supportive community may be the best fit. I've actually confirmed that this works well in LLaMa 7b. cpp is a way to use 4-bit quantization to reduce the memory requirements and speed up the inference. GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. koboldcpp. Click the Refresh icon next to Model in the top left. As far as I'm aware, GPTQ 4-bit w/ Exllama is still the best option. llama. 1 results in slightly better accuracy. cpp / GGUF / GGML / GPTQ & other animals. Model Developers Meta. jsons and . 4bit means how it's quantized/compressed. Quantize Llama models with GGML and llama. EDIT - Just to add, you can also change from 4bit models to 8 bit models. ago. It's a single self contained distributable from Concedo, that builds off llama. The uncensored wizard-vicuna-13B GGML is using an updated GGML file format. Please see below for a list of tools known to work with these model files. Under Download custom model or LoRA, enter TheBloke/vicuna-13B-1. 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. 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. Damp %: A GPTQ parameter that affects how samples are processed for quantisation. pt file into a ggml. # GPT4All-13B-snoozy-GPTQ This repo contains 4bit GPTQ format quantised models of Nomic. 58 seconds. Under Download custom model or LoRA, enter TheBloke/stable-vicuna-13B-GPTQ. Supports transformers, GPTQ, AWQ, EXL2, llama. Maybe now we can do a vs perplexity test to confirm. I have an Alienware R15 32G DDR5, i9, RTX4090. GGML vs GPTQ — Source:1littlecoder 2. in the download section. Supports NVidia CUDA GPU acceleration. llama2-wrapper. However, we made it in a continuous conversation format instead of the instruction format. ) Prompts Various (I'm not actually posting the question/answers it's irreverent for this test as we are checking speeds. Learn more about TeamsRunning a 3090 and 2700x, I tried the GPTQ-4bit-32g-actorder_True version of a model (Exllama) and the ggmlv3. Using MythoLogic-L2's robust understanding as its input and Huginn's extensive writing capability as its output seems to. After installing the AutoGPTQ library and optimum ( pip install optimum ), running GPTQ models in Transformers is now as simple as: from transformers import AutoModelForCausalLM model = AutoModelForCausalLM. Click the Model tab. For more general-purpose projects that require complex data manipulation, GPTQ's flexibility and extensive capabilities. Text Generation • Updated Sep 27 • 15. 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. However, I was curious to see the trade-off in perplexity for the chat. In addition to defining low-level machine learning primitives (like a tensor. cpp. 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. 1 results in slightly better accuracy. Because of the different quantizations, you can't do an exact comparison on a given seed. GPTQ can lower the weight precision to 4-bit or 3-bit. 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). According to open leaderboard on HF, Vicuna 7B 1. 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. This video explains difference between GGML and GPTQ in AI models in very easy terms. For Kobold CCP you use GGML files insted of the normal gptq or f16 formats. convert-gptq-ggml. GPTQ quantization [Research Paper] is a state of the art quantization method which results in negligible perfomance decrease when compared to previous quantization methods. 01 is default, but 0. In the top left, click the refresh icon next to. And I dont think there is literally any faster GPU out there for inference (VRAM Limits excluded) except H100. In short -- ggml quantisation schemes are performance-oriented, GPTQ tries to minimise quantisation noise. Supports transformers, GPTQ, AWQ, EXL2, llama. One quantized using q4_1, another one was quantized using q5_0, and the last one was quantized using q5_1. This is the repository for the 7B pretrained model, converted for the Hugging Face Transformers format. `A look at the current state of running large language models at home. Benchmark Execution: Running benchmarks on identical tasks using both SYCL and CUDA forms the foundation of performance comparison. In the table above, the author also reports on VRAM usage. The speed was ok on both (13b) and the quality was much better on the "6. GPTQ, AWQ, and GGUF are all methods for weight quantization in large language models (LLMs). bin. 0-GPTQ. 开箱即用，选择 gpt4all，有桌面端软件。. Probably would want to just call the stuff directly and save the inference test. I don't have enough VRAM to run the GPTQ one, I just grabbed the. What is gpt4-x-alpaca? gpt4-x-alpaca is a 13B LLaMA model that can follow instructions like answering questions. ) 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. 1]}. model files. After the initial load and first text generation which is extremely slow at ~0. 19】:1. cpp (GGUF), Llama models. Edit model. Last week, Hugging Face announced that Transformers and TRL now natively support AutoGPTQ. 注：如果模型参数过大无法. ggml's distinguishing feature is efficient operation on CPU. They collaborated with LAION and Ontocord to create the training dataset. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. The huge thing about it is that it can offload a selectable number of layers to the GPU, so you can use whatever VRAM you have, no matter the model size. Click the Refresh icon next to Model in the top left. Click Download. float16 HF format model for GPU inference. bitsandbytes: VRAM Usage. ) Test 3 TheBloke_Wizard-Vicuna-13B-Uncensored-GPTQ GPTQ-for-LLaMa The first one is to be installed when you want to load and interact with GPTQ models; the second one is to be ued with GGUF/GGML files, that can run on CPU only. Llama 2. This is the repository for the 70B pretrained model, converted for the Hugging Face Transformers format. Open the text-generation-webui UI as normal. from_pretrained ("TheBloke/Llama-2-7B-GPTQ") Run in Google Colab. I think that's a good baseline to. 5 (16k) is fine-tuned from Llama 2 with supervised instruction fine-tuning and linear RoPE scaling. 01 is default, but 0. It is a successor to Llama 1, which was released in the first quarter of 2023. 0. cpp) can. It can load GGML models and run them on a CPU. The model will start downloading. The Exllama_HF model loader seems to load GPTQ models. GGML, GPTQ, and bitsandbytes all offer unique features and capabilities that cater to different needs. 16 tokens per second (30b), also requiring autotune. from_pretrained ("TheBloke/Llama-2-7b-Chat-GPTQ", torch_dtype=torch. 0-Uncensored-GGML or if you have a GPU with 8 GB of VRAM use the GPTQ version instead of the GGML version. Quantization: Denotes the precision of weights and activations in a model. 33B you can only fit on 24GB VRAM, even 16Gb are not enough. conda activate vicuna. TheBloke/guanaco-65B-GGML. Combining Wizard and Vicuna seems to have strengthened the censoring/moralizing stuff each inherited from fine-tuning with Open ClosedAI's ChatGPT even more. pip install ctransformers [gptq] Load a GPTQ model using: llm = AutoModelForCausalLM. GPTQ vs. 0 dataset. Quantize your own LLMs using AutoGPTQ. During GPTQ I saw it using as much as 160GB of RAM. Setup python and virtual environment. Double quantization is. Wait until it says it's finished downloading. 1 results in slightly better accuracy. Albeit useful techniques to have in your skillset, it seems rather wasteful to have to apply them every time you load the model. Using a dataset more appropriate to the model's training can improve quantisation accuracy. CPP models (ggml, ggmf, ggjt) All versions of ggml ALPACA models (legacy format from alpaca. The original WizardLM, a 7B model, was trained on a dataset of what the creators call evolved instructions. TheBloke/SynthIA-7B-v2. 9 min read. The results below show the time it took to quantize models using GPTQ on an Nvidia A100 GPU. • 5 mo. Note that some additional quantization schemes are also supported in the 🤗 optimum library, but this is out of scope for this blogpost. 1. Under Download custom model or LoRA, enter TheBloke/falcon-40B-instruct-GPTQ. 4375 bpw. test. 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). I’ve tried the 32g and 128g and both are problematic. 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. cpp team on August 21st 2023. We will try to get in discussions to get the model included in the GPT4All. panchovix. cpp. Click the Model tab. In the Model dropdown, choose the model you just downloaded: Luna-AI-Llama2-Uncensored-GPTQ. Note that the GPTQ dataset is not the same as the dataset. LLM: quantisation, fine tuning. It is now able to fully offload all inference to the GPU. pt: Output generated in 113. cpp (GGUF), Llama models.