Convert gptq to gguf

cppでサポートできるようになる。. This repo contains GGUF format model files for Eric Hartford's Wizard-Vicuna-30B-Uncensored. The integration comes with native RoCm support for AMD GPUs. We provide a simple example of how to launch OpenAI-API compatible API with vLLM and Qwen2-7B-Instruct-GPTQ-Int8: Feb 1, 2024 · In the command above, we had to specify the user (TheBloke), repository name (zephyr-7B-beta-GGUF) and the specific file to download (zephyr-7b-beta. llama-2-13b-Q4_K_S. model = AutoModelForSeq2SeqLM. py -h Convert the HF model to GGUF model: python llama. `gpt2` or `facebook/wav2vec2-base-960h`", OpenVINO Intermediate Representation (IR) is the proprietary model format of OpenVINO. In this tutorial, You'll learn everything from:1. I am using the ctransformers lib and gguf files to generate the text and it looks like, whatever the model might be, the context length is capped at 512 tokens. For quality AWQ>GPTQ. Beginners. For the q5_k_m model, due to maximum file size for uploading, we split the GGUF file into 2. py -h. py does not treat tokenizer correctly just like last time with qwen1. gguf The remaining model types (like 16-bit transformers models and GPTQ models) are made of several files and must be placed in a subfolder. cpp much better and it's almost ready. In text-generation-webui. haotian-liu closed this as completed on Oct 17, 2023. I recommend using the huggingface-hub Python library: Sep 23, 2023 · However, if your primary concern is efficiency, GPTQ is the optimal choice. io. GGUF is a new format introduced by the llama. I recommend using the huggingface-hub Python library: . GPTQ. Also, llama. Feb 17, 2024 · 20240703 更新：llama. It allows to generate Text, Audio, Video, Images. This is supported by most GPU hardwares. Download and convert a model to GGUF using an imatrix, offloading 200 layers: quantkit gguf TinyLlama/TinyLlama-1. cpp: llava-v1. Convert the HF model to GGUF model: python llama. model = AutoModelForCausalLM. gguf --output mixtral-q2k. Nov 14, 2023. GGUF is going to make llama. c-seeger closed this as completed Apr Oct 31, 2022 · 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. py has been moved to examples/convert_legacy_llama. gguf file, or the directory containing sharded . It is also possible to download via the command-line with python download-model. 1B-1T-OpenOrca-GGUF tinyllama-1. 0 -out TinyLlama-1. Now, let's talk about the real game-changer - EXL2. I'll guide you th Nov 19, 2023 · The generation is very fast (56. readthedocs. This platform is designed to let your quant fit precisely into your GPU, unleashing the Nov 6, 2023 · Quantize the model using auto-gptq, U+1F917 transformers, and optimum. KoboldCpp is an easy-to-use AI text-generation software for GGML and GGUF models. hello. cpp downloaded from TheBloke. bearn01d added the bug-unconfirmed label on Jan 12. Oct 5, 2023 · you are dealing with a lora, which is an adapter for a model. It’s also designed for rapid model loading. cpp transpose. GGML is designed for CPU and Apple M series but can also offload some layers on the GPU. --local-dir-use-symlinks False. py vicuna-hf \. E. GGUF is a binary format that is designed for fast loading and saving of models, and for ease of reading. Sep 1, 2023 · git clone https://github. 1B-Chat-v1. Nov 14, 2023 · GGUF: GPT-Generated Unified Format. サポートするモデルは段階的に増える予定 We would like to show you a description here but the site won’t allow us. Except they had one big problem: lack of flexibility. Model conversion API translates the frequently used deep learning operations to their respective similar representation in OpenVINO and tunes them with the associated weights and biases from I recommend using the huggingface-hub Python library: pip3 install huggingface-hub. 5 16K - GGUF Model creator: lmsys; Original model: Vicuna 13B v1. You can quantize a model by using from_pretrained and setting the quantization_config. text-generation-webui └── models └── llama-2-13b-chat. This is the repository for the 7B pretrained model, converted for the Hugging Face Transformers format. I've tried googling around but I can't find a lot of info, so I wanted to ask about it. Users llama-2-13b-GPTQ-4bit-128g-actorder: Created with AutoGPTQ, bits=4, group_size=128, desc_act=True, wikitext-2-raw-v1 as the calibration file. /convert. cpp convert. from_pretrained( model_name, trust_remote_code=True, torch Jan 12, 2024 · Adaptation would be very desirable, but also needs to include adaptation to the new model schema including renamed layers. It looks like that if we want to convert the alpaca native GPTQ models we need to create a new tokenizer. A Zhihu column offering insights and discussions on various topics. /phi-2/model-00001-of-00002. No GPU required. domain-specific), and test settings (zero-shot vs. Closing this now. While you can’t quantize Llama 2 with GPTQ on the Google Colab free tier. Converting your models to GGUF format involves a few steps but fret not; the process is This repo contains GGUF format model files for Jon Durbin's Airoboros L2 7B Gpt4 2. GGUF: GPT-Generated Unified Format Nov 14, 2023 · GGUF: GPT-Generated Unified Format. 85 quants the best. Mar 25, 2024 · Quantization with GPTQ is also slow. Load th Llama 2. cpp team on August 21st 2023. Under Download Model, you can enter the model repo: TheBloke/Starling-LM-7B-alpha-GGUF and below it, a specific filename to download, such as: starling-lm-7b-alpha. You can report the problematic cases and see Feb 29, 2024 · GGUF in a Nutshell. 2. you can also merge the lora into the base model using the export-lora program. gguf IQ4_XS --built-in-imatrix -ngl 200. It is produced after converting a model with model conversion API. Quantization. then you can load the model and the lora. py, helps move models from GGML to GGUF smoothly. Code to convert a Model to GGML Format Nov 12, 2023 · GPTQ is the most often used compression method since it optimizes for GPU usage. The Ollama library contains a wide range of models that can be easily run by using the commandollama run <model Apr 1, 2024 · The GGUF format is also optimized for CPU speed (GPU is also supported), ensuring that your models run as efficiently as possible. The first argument after command should be an HF repo id (mistralai/Mistral-7B-v0. --local-dir-use-symlinks A GGUF model now remembers exactly what is it's native context size, and when you specify diffrent --ctx-size llamacpp automatically comapres those two, and calculates rope-freq for you, etc. ObamaNumber of tokens (514) exceeded maximum context length (512). A simple way to quantize a model can be done by first finding the maximum and minimum values of the weights, then dividing the Mar 21, 2023 · The output starts out decent, but quickly degrades into gibberish. GGUF principles guarantee that all essential information for model loading is encapsulated within a single file. GGUF vs. py "model path" c4 --wbits 4 --save_safetensors mode_name. 1) or a local directory with model files in it already. It took 35 min with one A10, The quantization speed and VRAM/RAM consumption are the same for the 4-bit, 3-bit, and 2-bit precisions. com/ggerganov/llama. Some models utilize a Byte-Pair encoding (bpe) tokenizer. Jan 25, 2024 · Help to converto to gguf - Beginners - Hugging Face Forums. Nov 16, 2023 · Changing from GGML to GGUF is made easy with guidance provided by the llama. This just isn't feasible for most people. cpp#3645. Optionally, Upload GGUF model to HuggingFace Models repo. Fetch a HuggingFace model. safetensors. Apr 21, 2023 · CUDA_VISIBLE_DEVICES=0 python gptneox. py with LLaMA 3 downloaded from Hugging Face. When trying to convert with llamacpp I have : (sati) rob@Robins-MBP-2 llama. cpp to run Qwen1. /phi-2 Loading model file . cpp allow users to easily share models in a single file. Quantizing helps improve inference speed, but it can negatively impact quality. 5-7B-Chat --outfile models/7B/qwen1_5-7b-chat-fp16. co/TheBlokeQuantization from Hugging Face (Optimum) - https://huggingface. HQQ offers competitive quantization accuracy while being very fast and cheap to quantize and not relying on a calibration dataset. Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. cpp 有公告 convert. from transformers import AutoModelForCausalLM. 4. The GPTQ quantization consumes a lot of GPU VRAM, for that reason we need to execute it in an A100 GPU in Colab. It is definitely worth starting with GPTQ and switching over to a CPU-focused method, like GGUF if your GPU cannot handle such large models. py on the llama. Then click Download. I've been going down huggingface's leaderboard grabbing some of I tend to get better perplexity using GGUF 4km than GPTQ even at 4/32g. gguf We demonstrate how to use llama. The . cpp, and adds a versatile Kobold API endpoint, additional format support, Stable Diffusion image generation, backward compatibility, as well as a fancy UI with persistent stories, editing tools, save formats, memory, world info, author text-generation-webui ├── models │ ├── llama-2-13b-chat. safetensors model files into *. The default vocabtype is 'spm' which invokes a Sentence Piece tokenizer. This is the repository for the 70B pretrained model, converted for the Hugging Face Transformers format. It's a single self contained distributable from Concedo, that builds off llama. safetensors GGUF is a file format for storing models for inference with GGML and executors based on GGML. Then you can download any individual model file to the current directory, at high speed, with a command like this: huggingface-cli download TheBloke/deepseek-coder-33B-instruct-GGUF deepseek-coder-33b-instruct. Basically, 4-bit quantization and 128 groupsize are recommended. py --input mixtral-8x7b-instruct-v0. In my case, the LLM returned the following output: Llama 2. Also, with Bonito, you can use it to generate datasets from unstructured text. cpp when using weights quantized by its own quantizer. Q2_K. Links to other models can be found in the index at the bottom. i used convert. Also with voice cloning capabilities. 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。. ️ 1 c-seeger reacted with heart emoji 🚀 1 c-seeger reacted with rocket emoji. py to convert but this generated a keyerror: intermediate_size. 1-AWQ. Sep 12, 2023 · 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 . Aug 28, 2023 · GPTQ is a specific format for GPU only. py Qwen/Qwen1. Owner. What sucks with GGUF is the context re-processing. AWQ) Exploring Pre-Quantized Large Language Models. This makes it particularly suitable for language models, which often 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. You can now basically, just run llamacpp giving it only the model file and the prompt. gguf. gguf --local-dir . Important args:--input: Path to the single . To learn more about quantizing model, read this documentation Oct 30, 2023 · There are 2 main formats for quantized models: GGML (now called GGUF) and GPTQ. Loaded through ExLlama v1. For Wl, Xl the weight matrix and the input of layer l respectively. cpp GitHub repo. gguf Jan 16, 2024 · Three prominent quantization methods—GPTQ, AWQ, and GGUF—stand out as contenders in the pursuit of achieving efficient and streamlined inference on Mistral 7B. It is also supports metadata, and is designed to be extensible. Of course there can be a graceful sunsetting of GPTQ to support older cards but supplying 6 versions of GPTQ model per repo while exl2 quanting suffers as a kind of cottage industry is just Nov 21, 2023 · III. Drop-in replacement for OpenAI running on consumer-grade hardware. Mar 13, 2024 · hqq Download and/or convert a model to HQQ format. txt. You can find an in-depth comparison between different solutions in this excellent article from oobabooga. Here is an incomplate list of clients and libraries that are known to support GGUF: llama. tensorflow. where the first argument refers to the path to the HF model directory or the HF model name, and the second argument refers Sep 29, 2023 · I have tried to convert the model using the llama. Now the quality for both 7b and 13b are improved. ローカルLLMの量子化フォーマットとしては、llama. in-context Aug 24, 2023 · The script is not intended to be the main method of creating GGUF models. 「. 1-GGUF mistral-7b-v0. Simple utility tool to convert automatically some weights on the hub to `safetensors` format. It is intended to be a backup for those who don't have the hardware to create the GGUF model from scratch. Under Download Model, you can enter the model repo: TheBloke/Llama-2-7B-GGUF and below it, a specific filename to download, such as: llama-2-7b. cpp can use the CPU or the GPU for inference (or both, offloading some layers to one or more GPUs for GPU inference while leaving others in main memory for CPU inference). You could not add additional information about the Oct 11, 2023 · haotian-liu commented on Oct 17, 2023. Q4_K_M. gguf: Q8_0: 5: 36. Note that some additional quantization schemes are also supported in the 🤗 optimum library, but this is out of scope for this blogpost. 1 -out Mistral-7B-v0. py . You need to first make a GGUF file for the fp16 model as shown below: python convert-hf-to-gguf. cpp provides a converter script for turning safetensors into GGUF. It works by protecting salient weights by observing the activation, not the weights themselves. See here for a more complete list of the benefits of using GGUF. You can also export quantization parameters with toml+numpy format. cpp or GPTQ. Note: convert. Usage of GPTQ Quantized Models with vLLM¶ vLLM has supported GPTQ, which means that you can directly use our provided GPTQ models or those trained with AutoGPTQ with vLLM. Merged. ）がllama. gguf). We will explore the three common methods for Subreddit to discuss about Llama, the large language model created by Meta AI. 1b-1t-openorca Feb 19, 2024 · I guess convert-hf-to-gguf. py script on the HuggingFace model. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. This comes without a big drop of performance and with faster inference speed. ggerganov mentioned this issue on Jan 12. if you want to use the lora, first convert it using convert-lora-to-ggml. The download command defaults to downloading into the HF cache and producing symlinks in the output dir, but there is a --no-cache option which places the Nov 23, 2023 · In this tutorial, we will explore many different methods for loading in pre-quantized models, such as Zephyr 7B. 0. This tool, found at convert-llama-ggml-to-gguf. i used SFTTrainer to fine tune a model (opt-350m) with a dataset, now i have a folder with several files and a model. About GGUF GGUF is a new format introduced by the llama. It is PyTorch exclusive for now. Jun 20, 2023 · Quantization can be applied to existing models trained with 32-bit floats, by converting the weights to smaller integer primitives that will still benefit from hardware accelerated instruction sets like Intel's AVX. bin files that are used by llama. gguf files. 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 text-generation-webui. It takes about 45 minutes to quantize the model, less than $1 in Colab. Then you can download any individual model file to the current directory, at high speed, with a command like this: huggingface-cli download TheBloke/Mistral-7B-v0. py. This doesn't happen with either the original GPTQ-for-LLaMa using the same weights, or llama. GGUF) Thus far, we have explored sharding and quantization techniques. Push the newly created GPTQ Models to HF Transformers3. が、たまに量子化されていない Couple that with the flexibility of quants other than 4-bit and you have a very compelling case for exl2 as a replacement for GPTQ and a companion to GGUF. Installing Ollama. convert : update phi-2 to latest HF repo #4903. danportu January 25, 2024, 10:30am 1. これにより、Llama以外の言語モデル（falcon, rwkv, bloom, etc. PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python python convert_gguf_to_torch. Download and convert a model to AWQ: quantkit awq mistralai/Mistral-7B-v0. json) except the prompt template * llama. as a PR on the hub. Converting a Pytorch LLM into GPTQ Models2. GGUFは、GGMLよりも拡張性の高いファイルフォーマット。. check qwen. gguf In both cases, you can use the "Model" tab of the UI to download the model from Hugging Face automatically. GPTQ drastically reduces the memory requirements to run LLMs, while the inference latency is on par with FP16 inference. The approach Under Download Model, you can enter the model repo: TheBloke/Llama-2-13B-chat-GGUF and below it, a specific filename to download, such as: llama-2-13b-chat. It does not support LLaMA 3, you can use convert_hf_to_gguf. g. I see official qwen1. py following the colab note HERE. I recommend using the huggingface-hub Python library: Aug 20, 2023 · フォーマット変更の要点. Runs gguf, transformers, diffusers and many more models architectures. git. Under Download Model, you can enter the model repo: TheBloke/Chronoboros-33B-GGUF and below it, a specific filename to download, such as: chronoboros-33b. With GPTQ quantization, you can quantize your favorite language model to 8, 4, 3 or even 2 bits. gguf \ --outtype q8_0 In this case we're also quantizing the model to 8 bit by setting --outtype q8_0. You could adapt this for pytorch by replacing it with a pytorch state dictionary. At a higher level, the process involves the following steps: Install the requirements for the below process. Models are traditionally developed using PyTorch or another framework, and then converted to GGUF for use in GGML. 🤖 The free, Open Source OpenAI alternative. Feb 18, 2024 · The idea here is you can get the original LLaMA model, convert it to GGUF format and finally, quantize GGUF format to lower precision. AWQ takes an activation-aware GGUF does not need a tokenizer JSON; it has that information encoded in the file. GPTQ, a one-shot weight quantization method, harnesses approximate second-order information to achieve highly accurate and efficient quantization. model that has this "PAD" token in it. co/docs/optimum/ Nov 4, 2023 · This novel development allows users to effectively apply GPTQ quantization, enabling the quantization of preferred language models to 8, 4, 3, or even 2 bits. Mar 29, 2024 · With these steps and examples, you now learn how to download a Huggingface Pytorch model, convert it to GGUF, quantize it, contribute/upload it on Huggingface then run it with Ollama. 9. gguf: q4_K_M quant for llama. Essentially, we split a byte string to 2, and thus you can just concatenate them to get the whole file: cat qwen1_5-72b-chat-q5_k_m. We also outperform a recent Triton implementation for GPTQ by 2. 1B-IQ4_XS. In this tutorial, I dive deep into the cutting-edge technique of quantizing Large Language Models (LLMs) using the powerful llama. Actually, the usage is the same with the basic usage of vLLM. Q5_K_M. Aug 29, 2023 · 2023年8月28日 13:33. 5 16K; Description This repo contains GGUF format model files for lmsys's Vicuna 13B v1. 6-34b. from_pretrained(model_id, quantization_config=gptq_config) Note that you will need a GPU to quantize a model. * > qwen1_5-72b-chat-q5_k_m. 5 repo work through and i do not know why. It takes about 180 seconds to generate 45 tokens(5->50 tokens) on single RTX3090 based on LLaMa-65B. help = "The name of the model on the hub to convert. py organization/model (use --help to see all the options). . llama-2-13b-Q4_K_M. . 👀 1. Nov 13, 2023 · Quantization is a powerful technique to reduce the memory requirements of a model whilst keeping performance similar. Under Download Model, you can enter the model repo: TheBloke/CodeLlama-7B-GGUF and below it, a specific filename to download, such as: codellama-7b. 65 bpw. The intended method of creating GGUF models is to convert HF models directly to GGUF, which requires loading the full HF model. 1. gguf Mar 4, 2024 · Then, we have to convert this model to the GGUF format, before quantization, in FP16. 44 tokens/second on a T4 GPU), even compared to other quantization techniques and tools like GGUF/llama. Thank you! In text-generation-webui. Quantization is far from being a solved problem. 5: Vicuna 13B v1. GGUF is an advanced binary file format for efficient storage and inference with GGML, a tensor library for machine learning written in C. bin」から「. 5-7B-Chat. I recommend using the huggingface-hub Python library: Jan 21, 2024 · I converted the PyTorch model to GGUF in FP16 weights. cpp has a script to convert *. GGUF, previously GGML, is a quantization method that allows users to use the CPU to run an LLM but also offload some of its layers to the GPU for a speed up. This approach aims to reduce model size by converting… Sep 9, 2023 · First convert the gguf to torch state dict and tokenizer file using the code in the examples folder. cpp Sep 7, 2023 · Quantized models are serializable and can be shared on the Hub. To convert a BPE-based model, use this syntax: convert. 5 GB: very large, extremely low quality loss - not recommended: ORIGINAL LLaVA Model Card Model details Aug 3, 2023 · Learning Resources:TheBloke Quantized Models - https://huggingface. Then you can download any individual model file to the current directory, at high speed, with a command like this: huggingface-cli download TheBloke/TinyLlama-1. py (GPTQ vs. Q8_0. q4_K_M. Pre-Quantization (GPTQ vs. Assignees. AWQ vs. We can do this with the script convert-hf-to-gguf. AWQ) Exploring Mar 23, 2023 · 32001 is the n_vocab (Number of tokens on the model) The model that is trained with alpaca has 1 more token and it's this one: " [PAD]": 32000. add_tensor(layer, tmp_tensor) 🤗 Optimum collaborated with AutoGPTQ library to provide a simple API that apply GPTQ quantization on language models. Self-hosted, community-driven and local-first. You can try also adding act-order and true-sequential or group size, just don't put act order + group size together. Although GPTQ does compression well, its focus on GPU can be a disadvantage if you do not have the hardware to run it. llama. Oooba's more scientific tests show that exl2 is the best format though and it tends to subjectively match for me on >4. Sep 18, 2023 · I have found a solution for this problem. Also the main speed comparison should be considering batch size and rolling batches/streaming batches. AutoGPTQ supports Exllama kernels for a wide range of architectures. llama-2-13b-GPTQ-4bit-32g-actorder: Same as above but with group_size=32. 4× since it relies on a high-level language and forgoes opportunities for low-level optimizations. py vicuna-hf \ --outfile vicuna-13b-v1. GGML/GGUF is a C library for machine learning (ML) — the “GG” refers to the initials of its originator (Georgi Now, suppose you would like to quantize Qwen1. The bug seems to be fixed in ggerganov/llama. cpp/requirements. I recommend using the huggingface-hub Python library: pip3 install huggingface-hub. Things should work fine with convert-hf-to-gguf. fixing convert-hf-to-gguf. Specifically, GPTQ can quantize GPT models with 175 billion parameters in approximately four GPU hours, reducing the bitwidth down to 3 or 4 bits GPTQ/AWQ is tailored for GPU inferencing, claiming to be 5x faster than GGUF when running purely on GPU. py 棄用了，要改用 convert-hf-to-gguf. Just run Bonito with Ollama, and use LangChain to organize the dataset generation. Ollama is a tool that helps us run llms locally. The Jan 26, 2024 · tak2hu commented on Jan 25. py provided by llama. GPTQ is a post-training quantization approach that aims to solve the layer-wise quantization problem. Is there a bug in the conversion script that somehow only comes into play with a large context size? I did notice one potential issue. (GPTQ vs. I had mentioned on here previously that I had a lot of GGMLs that I liked and couldn't find a GGUF for, and someone recommended using the GGML to GGUF conversion tool that came with llama. Then when I got around to trying to quantize it (with out that runs pretty slowly and is much larger), I found that cMake wasn’t available on my Windows system. To convert, run examples/gguf_to_torch. Then start up the vllm server as usual. I like those 4. cpp % python3 . (it requires the base model). tokens (513)exceeded maximum context length (512). It is a replacement for GGML, which is no longer supported by llama. 5 16K. Executing the llama. We would like to show you a description here but the site won’t allow us. On the command line, including multiple files at once. Step-by-Step Conversion to GGUF Using llama. Aug 31, 2023 · python llama. Smaller models (<4B parameters) can be quantized with a colab-free tier. Jan 22, 2024 · GPTQ is a technique for compressing deep learning model weights through a 4-bit quantization process that targets efficient GPU inference. get_tensor(layer) gguf_writer. py and shouldn't be used for anything other than Llama/Llama2/Mistral models and their derivatives. AWQ outperforms round-to-nearest (RTN) and GPTQ across different model scales (7B-65B), task types (common sense vs. Number of tokens (515) exceeded maximum context length (512). py modelname_or_path --vocabtype bpe--vocab-type Nov 2, 2023 · AWQ (Activation-Aware Weight Quantization) is a more recent quantization method that outperforms GPTQ and GGUF in terms of compression gains and inference speed for language models. cpp tool. cpp/convert. cpp. Also EXL with different calibration sets blows shit away. cpp repo is needed. You can find the code in this notebook in my repository. safetensors (quantized using GPTQ algorithm) AWQ (low-bit quantization (INT3/4)) safetensors (using AWQ algorithm) Notes: * GGUF contains all the metadata it needs in the model file (no need for other files like tokenizer_config. I recommend using the huggingface-hub Python library: Pre-conversion has the advantage of faster loading time after conversion, saves a lot of system RAM when using multiple GPUs, and support model architectures besides LLAMA. Verify the script is there and understand the various options: python llama. It allows for faster loading, using, and fine-tuning LLMs even with smaller GPUs. pre_layer is set to 50. cpp (GGUF/GGML)とGPTQの2種類が広く使われている。. 5. A bit unrelated, I tried converting a (pytorch) safetensors model into ggml by following the gguf-py example. This provides a significant speed boost for those who rely heavily on GPU power for their models. gguf」になる。. II. Install the required python libraries: pip install -r llama. tmp_tensor = model_st. rh po ma np mp as tt bv gm dv