but when i run ggml it just seems so much slower than GPTQ versions. (2) And does the mean we'd do well to download new GPTQ quants of our favorite models in light of the new information? (3) I'm also still a bit curious of GGML is competitive with GPTQ/exllama when running on Nvidia GPU. A detailed comparison between GPTQ, AWQ, EXL2, q4_K_M, q4_K_S, and load_in_4bit: perplexity, VRAM, speed, model size, and loading time. cpp is a project that uses ggml to run Whisper, a speech recognition model by OpenAI. Sol_Ido. Another day, another great model is released! OpenAccess AI Collective's Wizard Mega 13B. GPT-2 (All versions, including legacy f16, newer format + quanitzed, cerebras) Supports OpenBLAS acceleration only for newer format. I worked with GPT4 to get it to run a local model, but I am not sure if it hallucinated all of that. This end up using 3. There's just something unusual/different causing it not to work for you guys as a GPTQ on 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. Use both exllama and GPTQ. Along with most 13B models ran in 4bit with around Pre-layers set to 40 in Oobabooga. *Its technically not compression. Maybe now we can do a vs perplexity test to confirm. To use with your GPU using GPTQ pick one of the . GPTQ. Hugging Face. The default templates are a bit special, though. 2 toks. 2x. The metrics obtained include execution time, memory usage, and. 4bit means how it's quantized/compressed. 35 2,669 9. Supports transformers, GPTQ, AWQ, EXL2, llama. GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Under Download custom model or LoRA, enter TheBloke/Wizard-Vicuna-13B-Uncensored-SuperHOT-8K-GPTQ. This is wizard-vicuna-13b trained with a subset of the dataset - responses that contained alignment / moralizing were removed. Since the original full-precision Llama2 model requires a lot of VRAM or multiple GPUs to load, I have modified my code so that quantized GPTQ and GGML model variants (also known as llama. So far, I've run GPTQ and bitsandbytes NF4 on a T4 GPU and found: fLlama-7B (2GB shards) nf4 bitsandbytes quantisation: - PPL: 8. It's the current state-of-the-art amongst open-source models. 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. 256 70 2,931 contributions in the last year Contribution Graph; Day of Week: November Nov: December Dec: January Jan: February Feb: March Mar: April Apr: May May: June Jun:. The zeros and. OpenLLaMA is an openly licensed reproduction of Meta's original LLaMA model. Note at that time of writing this documentation section, the available quantization methods were: awq, gptq and bitsandbytes. AWQ, on the other hand, is an activation. Right, those are GPTQ for GPU versions. However, on 8Gb you can only fit 7B models, and those are just dumb in comparison to 33B. Open comment sort options. Click Download. cpp. First, we explore and expand various areas in the same topic using the 7K conversations created by WizardLM. Oobabooga's got bloated and recent updates throw errors with my 7B-4bit GPTQ getting out of memory. 4375 bpw. The current release includes the following features: An efficient implementation of the GPTQ algorithm: gptq. cppを選ぶメリットが減ってしまう気もする（CPUで動かせる利点は残るものの）。 なお個人の使用実感でいうと、量子化によるテキストの劣化はあまり感じられない。In this blog post, our focus will be on converting models from the HuggingFace format to GGUF. The model will start downloading. Update 04. devops","path":". Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Uses that GPT doesn’t allow but are legal (for example, NSFW content) Enterprises using it as an alternative to GPT-3. Using MythoLogic-L2's robust understanding as its input and Huginn's extensive writing capability as its output seems to. Supporting model backends: tranformers, bitsandbytes(8-bit inference),. GGUF) Thus far, we have explored sharding and quantization techniques. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. Under Download custom model or LoRA, enter TheBloke/Wizard-Vicuna-30B-Uncensored-GPTQ. The change is not actually specific to Alpaca, but the alpaca-native-GPTQ weights published online were apparently produced with a later version of GPTQ-for-LLaMa. For GPTQ tests, I used models with groupsize 128 and no desc_act, which are the ones that are widely used. 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. This is the repository for the 7B pretrained model. Supports transformers, GPTQ, AWQ, EXL2, llama. 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. Agreed on the transformers dynamic cache allocations being a mess. jsons and . This end up using 3. Click Download. Locked post. Right, those are GPTQ for GPU versions. GPTQ clearly outperforms here. This adds full GPU acceleration to llama. Quantized in 8 bit requires 20 GB, 4 bit 10 GB. If we take any GPTQ model lets say Wizard Vicuna 13B. By reducing the precision ofGGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. But this should have been compensated by the various updates in the SIMD code. I have suffered a lot with out of memory errors and trying to stuff torch. Download 3B ggml model here llama-2–13b-chat. 4bit means how it's quantized/compressed. 4375 bpw. cpp (GGUF/GGML)とGPTQの2種類が広く使われている。. 0 license, with full access to source code, model weights, and training datasets. Pick yer size and type! Merged fp16 HF models are also available for 7B, 13B and 65B (33B Tim did himself. GGML files are for CPU + GPU inference using llama. Pros: GGML was an early attempt to create a file format for storing GPT models. 24 seconds. 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. 4bit and 5bit GGML models for CPU inference. GPTQ uses Integer quantization + an optimization procedure that relies on an input mini-batch to perform the quantization. 4bit and 5bit quantised GGML models for CPU inference - TheBloke/stable-vicuna-13B-GGML----- Prompt Template. 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. Please see below for a list of tools known to work with these model files. Context sizes: (512 | 1024 | 2048) ⨯ (7B | 13B | 30B | 65B) ⨯ (llama | alpaca[-lora] | vicuna-GPTQ) models, first 406 lines of wiki. github","path":". There are already bleeding edge 4-bit quantization efforts such as GPTQ for LLaMA. 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。. It has \"levels\" that range from \"q2\" (lightest, worst quality) to \"q8\" (heaviest, best quality). From what I've skimmed in their paper, GPTQ uses some tricky linear algebra not only to calculate the weights, but to also store them in some compressed way. cpp GGML models, so we can compare to figures people have been doing there for a while. model-specific. During GPTQ I saw it using as much as 160GB of RAM. and some compatibility enhancements. 5. mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices. • 5 mo. GPTQ supports amazingly low 3-bit and 4-bit weight quantization. 29. 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. 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. r/LocalLLaMA • (Code Released) Landmark Attention: Random-Access Infinite Context Length for Transformers. So for 7B and 13B you can just download a ggml version of Llama 2. A standalone Python/C++/CUDA implementation of Llama for use with 4-bit GPTQ weights, designed to be fast and memory-efficient on modern GPUs. Llama-2-7B-32K-Instruct is an open-source, long-context chat model finetuned from Llama-2-7B-32K, over high-quality instruction and chat data. 4375 bpw. 24 # GPU version!pip install ctransformers[gptq] On you computer: We also outperform a recent Triton implementation for GPTQ by 2. Env: Mac M1 2020, 16GB RAM Performance: 4 ~ 5 tokens/s Reason: best with my limited RAM, portable. Reply nihnuhname • Additional comment actions. Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Pygmalion 7B SuperHOT 8K GGML. cpp. Note that the GPTQ dataset is not the same as the dataset. text-generation-webui - A Gradio web UI for Large Language Models. I've actually confirmed that this works well in LLaMa 7b. Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Unique Merging Technique. . 3TheBloke/Wizard-Vicuna-30B-Uncensored-GPTQ. I'll be posting those this weekend. are other backends with their own quantized format, but they're only useful if you have a recent graphics card (GPU). Compare privateGPT vs GPTQ-for-LLaMa and see what are their differences. 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. 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. Loading: Much slower than GPTQ, not much speed up on 2nd load. 1. Under Download custom model or LoRA, enter TheBloke/airoboros-33b-gpt4-GPTQ. Because of the different quantizations, you can't do an exact comparison on a given seed. Output Models generate text only. GGML files are for CPU + GPU inference using llama. That was it's main purpose, to let the llama. The difference for LLaMA 33B is greater than 1 GB. I was told that if we quantize this model into five different final models. GGML 30B model VS GPTQ 30B model 7900xtx FULL VRAM Scenario 2. I can run TheBloke_Wizard-Vicuna-13B-Uncensored-GPTQ on that of a RTX 3060 12GB GPU. This is the repository for the 70B pretrained model, converted for the Hugging Face Transformers format. cpp CPU (+CUDA). GPTQ vs. I’m keen to try a ggml of it when that becomes possible to see if it’s a bug in my GPTQ files or. 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. I'm running models in my home pc via Oobabooga. Results. Repositories available 4bit GPTQ models for GPU inference. I got GGML to load after following your instructions. and that llama. Use both exllama and GPTQ. 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. 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. GPTQ is better, when you can fit your whole model into memory. Uses GGML_TYPE_Q5_K for the attention. A quick glance would reveal that a substantial chunk of these models has been quantified by TheBloke, an influential and respected figure in the LLM community. ggmlv3. 01 is default, but 0. OpenLLaMA is an openly licensed reproduction of Meta's original LLaMA model. Scales are quantized with 6 bits. In the Model drop-down: choose the model you just downloaded, falcon-40B-instruct-GPTQ. Models by stock have 16bit precision, and each time you go lower, (8 bit, 4bit, etc) you sacrifice some. Update 04. devops","contentType":"directory"},{"name":". Under Download custom model or LoRA, enter TheBloke/WizardCoder-15B-1. support for > 2048 context with any model without requiring a SuperHOT finetune merge. GGML files are for CPU + GPU inference using llama. 1 results in slightly better accuracy. SuperHOT is a new system that employs RoPE to expand context beyond what was originally possible for a model. Oobabooga: If you require further instruction, see here and hereStep 1: Request download. 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. Click Download. A general sentiment I’ve gotten from the community is that ggml vs gptq is akin to accuracy vs speed. As this is a GPTQ model, fill in the GPTQ parameters on the right: Bits = 4, Groupsize = 128, model_type = Llama. GPTQ dataset: The dataset used for quantisation. WolframRavenwolf • 3 mo. It can also be used with LangChain. 3-bit has been shown very unstable ( Dettmers and Zettlemoyer, 2023 ). This causes various problems. TheBloke/wizardLM-7B-GPTQ. Please specify it manually using --model_type argument Press any key to continue . All 3 versions of ggml LLAMA. This is the option recommended if you. These algorithms perform inference significantly faster on NVIDIA, Apple and Intel hardware. GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. GGML is the only option on Mac. GGUF, introduced by the llama. , 2023) was first applied to models ready to deploy. 1 results in slightly better accuracy. For reference, I'm used to 13B models generating at 2T/s, and 7B models at 4 T/s. bin: q3_K_L: 3: 3. It is a successor to Llama 1, which was released in the first quarter of 2023. GPTQ dataset: The dataset used for quantisation. GPTQ is an alternative method to quantize LLM (vs llama. cpp, or currently with text-generation-webui. Using MythoLogic-L2's robust understanding as its input and Huginn's extensive writing capability as its output seems to. Pygmalion 13B SuperHOT 8K GGML. after prompt ingestion). I have high hopes for an unfiltered mix like this, but until that's done, I'd rather use either vicuna-13b-free or WizardLM-7B-Uncensored alone. Wait until it says it's finished downloading. Click the Model tab. Untick Autoload the model. For example, from here: TheBloke/Llama-2-7B-Chat-GGML TheBloke/Llama-2-7B-GGML. GGML is designed for CPU and Apple M series but can also offload some layers on the GPU. But Vicuna 13B 1. Specifically, GPTQ can quantize GPT models with 175 billion parameters in approximately four GPU hours, reducing the bitwidth down to 3 or 4. With the Q4 GPTQ this is more like 1/3 of the time. 45/hour. I appear to be stuck. In the Model drop-down: choose the model you just downloaded, vicuna-13B-1. Using a dataset more appropriate to the model's training can improve quantisation accuracy. The model will automatically load, and is now ready for use! If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. Under Download custom model or LoRA, enter TheBloke/Wizard-Vicuna-7B-Uncensored-GPTQ. H2OGPT's OASST1-512 30B GGML These files are GGML format model files for H2OGPT's OASST1-512 30B. Env: Mac M1 2020, 16GB RAM. Input Models input text only. cpp (a lightweight and fast solution to running 4bit quantized llama models locally). But GGML allows to run them on a medium gaming PC at a speed that is good enough for chatting. This is what I used: python -m santacoder_inference bigcode/starcoderbase --wbits 4 --groupsize 128 --load starcoderbase-GPTQ-4bit-128g/model. Setup python and virtual environment. Their rate of progress is incredible. Albeit useful techniques to have in your skillset, it seems rather wasteful to have to apply them every time you load the model. gpt4-x-alpaca’s HuggingFace page states that it is based on the Alpaca 13B model, fine. Is it faster for inferences than the GPTQ format? You can't compare them because they are for different purposes. AutoGPTQ is a library that enables GPTQ quantization. In the Model dropdown, choose the model you just downloaded: WizardCoder-15B-1. I haven't tested perplexity yet, it would be great if someone could do a comparison. Click Download. 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. No matter what command I used, it still tried to download it. They take only a few minutes to create, vs more than 10x longer for GPTQ, AWQ, or EXL2, so I did not expect them to appear in any Pareto frontier. Click Download. Build whisper. This user has. GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. 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. Click the Model tab. 1 results in slightly better accuracy. The only way to convert a gptq. Just monitor your cpu usage vs gpu usage. In the Model dropdown, choose the model you just downloaded: Nous-Hermes-13B-GPTQ. < llama-30b FP16 2nd load INFO:Loaded the model in 39. In addition to defining low-level machine learning primitives (like a tensor. 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. Share Sort by: Best. Model Developers Meta. Click the Model tab. The latest version of llama. When comparing llama. GPTQ vs. Edit model. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":". Scales and mins are quantized with 6 bits. The older GGML format revisions are unsupported and probably wouldn't work with anything other than KoboldCCP since the Devs put some effort to offer backwards compatibility, and contemporary legacy versions of llamaCPP. 1 GPTQ 4bit runs well and fast, but some GGML models with 13B 4bit/5bit quantization are also good. My understanding was training quantisation was the big breakthrough with qlora, so in terms of comparison it’s apples vs oranges. If you mean running time - then that is still pending with int-3 quant and quant 4 with 128 bin size. That's it. cpp team on August 21, 2023, replaces the unsupported GGML format. It uses the same architecture and is a drop-in replacement for the original LLaMA weights. I'm also still a bit curious of GGML is competitive with GPTQ/exllama when running on Nvidia GPU. cpp - convert-lora-to-ggml. cpp (GGUF), Llama models. Updated to the latest fine-tune by Open Assistant oasst-sft-7-llama-30b-xor. In this case, you might try something like the following: llama2-base-13b-kimono. 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. We will use the 4-bit GPTQ model from this repository. New k-quant method. 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. GGML/GGUF is a C library for machine learning (ML) — the “GG” refers to. cpp users to enjoy the GPTQ quantized models. A discussion thread on GitHub that compares the performance of GGML, a generative model for text generation, with and without GPU acceleration and three different GPTQ. 01 is default, but 0. 0 dataset. 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. --Best--GGML Wizard Vicuna 13B 5_1 GGML Wizard Vicuna 13B 5_0 GPTQ Wizard Vicuna 13B 4bit GGML Wizard Vicuna. Context is hugely important for my setting - the characters require about 1,000 tokens apiece, then there is stuff like the setting and creatures. 01 is default, but 0. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Updated to the latest fine-tune by Open Assistant oasst-sft-7-llama-30b-xor. And it can be applied to LLaMa. 90 GB: True: AutoGPTQ: Most compatible. 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. KoboldAI (Occam's) + TavernUI/SillyTavernUI is pretty good IMO. float16, device_map="auto"). The model will automatically load, and is now ready for use!GGML vs. This will produce ggml-base. So it seems that GPTQ has a similar latency problem. 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. cpp, text-generation-webui or KoboldCpp. GGML to GGUF is the transition from prototype technology demonstrator to a mature and user-friendy solution. GGML/GGUF models are tailored to minimize memory usage rather than prioritize speed. GPTQ vs. , 2023) was first applied to models ready to deploy. Scales and mins are quantized with 6 bits. domain-specific), and test settings (zero-shot vs. During GPTQ I saw it using as much as 160GB of RAM. 0-GPTQ. Llama 2. In addition to defining low-level machine learning primitives (like a tensor type), GGML defines a binary format for distributing LLMs. Once it's finished it will say "Done". One quantized using q4_1, another one was quantized using q5_0, and the last one was quantized using q5_1. 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. FP16 (16bit) model required 40 GB of VRAM. 2023年8月28日 13:33. Here are the ggml versions: The unfiltered vicuna-AlekseyKorshuk-7B-GPTQ-4bit-128g-GGML and the newer vicuna-7B-1. GPTQ means the model is optimized to run on a dedicated GPU, while GGML is optimized to run on a CPU. Last week, Hugging Face announced that Transformers and TRL now natively support AutoGPTQ. cpp that introduced this new Falcon GGML-based support: cmp-nc/ggllm. I've recently switched to KoboldCPP + SillyTavern. Under Download custom model or LoRA, enter TheBloke/falcon-7B-instruct-GPTQ. And I dont think there is literally any faster GPU out there for inference (VRAM Limits excluded) except H100. json'. < llama-30b-4bit 1st load INFO:Loaded the model in 7. Untick Autoload model. Try 4bit 32G and you will more than likely be happy with the result! When comparing GPTQ-for-LLaMa and llama. GPU Installation (GPTQ Quantised) First, let’s create a virtual environment: conda create -n vicuna python=3. Devs playing around with it. My machine has 8 cores and 16 threads so I'll be. 0 GGML These files are GGML format model files for WizardLM's WizardCoder 15B 1. The paper explains it in more detail, but to summarize, complex instruct means exactly what it sounds like. 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. In GPTQ, we apply post-quantization for once, and this results in both memory savings and inference speedup (unlike 4/8-bit quantization which we will go through later). 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. In the top left, click the refresh icon next to. I've used these with koboldcpp, but CPU-based inference is too slow for regular usage on my laptop. Click Download. Not sure but after converting HF 7B int4 GPTQ to ggml bin format: Unfortunately it is not that simple. Untick Autoload model. privateGPT. 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). In the Model dropdown, choose the model you just downloaded: WizardCoder-Python-34B-V1. Models; Datasets; Spaces; DocsThis video explains difference between GGML and GPTQ in AI models in very easy terms. However, we made it in a continuous conversation format instead of the instruction format. These files will not work in llama. 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. Use llama2-wrapper as your local llama2 backend for Generative Agents/Apps, colab example. Supports transformers, GPTQ, AWQ, EXL2, llama. As quoted from this site. Documentation ConfigIt's working perfectly fine (and doing very well for a 7B) in HF, GGML and GPTQ formats for me. So I need to train a non-GGML, then convert the output. alpaca-lora - Instruct-tune LLaMA on consumer hardware. 8G. 0. Once it's finished it will say "Done". cpp (GGUF), Llama models. When you run this program you should see output from the trained llama. safetensors along with all of the . cpp team on August 21st 2023. Note that the GPTQ dataset is not the same as the dataset. . cpp) rather than having the script match the existing one: - The tok_embeddings and output. Click Download. 1. 2023. Damp %: A GPTQ parameter that affects how samples are processed for quantisation. GGML speed strongly depends on the performance and the positioning of RAM slots Reply. GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. github","path":". To download from a specific branch, enter for example TheBloke/Wizard-Vicuna-30B. 0-GPTQ. 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. Scales and mins are quantized with 6 bits. AI's original model in float32 HF for GPU inference. For my box with AMD 3700X, the 3090 only gets to 60-75% GPU. AWQ outperforms round-to-nearest (RTN) and GPTQ across different model scales (7B-65B), task types (common sense vs.