Llama cpp gptq example reddit

In general, I notice that when using GGUFs with llama. safetenors, act-order and no act-orders. They can fit almost completely in 12 GB so it's very fast. cpp is CPU, GPU, or mixed, so it offers the greatest flexibility. py (from llama. Also, llama. It takes about 180 seconds to generate 45 tokens(5->50 tokens) on single RTX3090 based on LLaMa-65B. It is a GUI application that utilizes GGUF models with a llama. !pip install langchain. q4_0) a go in llama. Personally I’m using the llama. : LocalLLaMA (reddit. Aug 23, 2023 · The AutoGPTQ library enables users to quantize 🤗 Transformers models using the GPTQ method. Performance: 46 tok/s on M2 Max, 156 tok/s on RTX 4090. I'm able to get about 1. If that's the case then the correct path would be D:/llama2-7b. cpp build runs it without issue as well. A GPTQ model should even inference faster than an equivalent-bitrate EXL2 model. cpp (details below) Question I have the same model (for example mixtral instruct 8x7B) quantized in 4bit: the first one is in safetensors, loaded with vLLM, and takes approximately 40GB GPU vRAM, and to make it usable I need to lower context to The paper shows that the AWQ-8 model is 4x smaller than the GPTQ-8 model, and the AWQ-4 model is 8x smaller than the GPTQ-8 model. cpp with ggml quantization to share the model between a gpu and cpu. cpp models are usually the fastest. cpp running on the llama-2-7b-chat. The local user UI accesses the server through the API. Here's a funny one. All the Llama models are comparable because they're pretrained on the same data, but Falcon (and presubaly Galactica) are trained on different datasets. compress_pos_emb is for models/loras trained with RoPE scaling. 52 ms / 182 runs ( 0. I think all I did was checkout the git repository under the repositories directory and probably installed the requirements. ) Reply reply FishKing-2065. cpp library -- setting a LLAMA_CPP_LIB environment variable before importing the package. cpp will calculate the appropriate Rope-scaling values automatically. Confirmed with Xwin-MLewd-13B-V0. cpp 8-bit through llamacpp_HF emerges as a good option for people with those GPUs until 34b gets released. cpp, the sampler settings do not seem to make too much of a difference, compared to ExLlama2 and GPTQ. More hardwares & model sizes coming soon! This is done through the MLC LLM universal deployment projects. cpp with temp=0. If you can't get it running, give the GPTQ version a try in the text-generation-webui. true. So why train a model with a template format that is not properly supported (currently) by llama. cpp GPU Offloading Not Working for me with Oobabooga Webui - Need Assistance. example of how to do that: Llama-2 has 4096 context length. I guess iq3 should fit. If/when you want to scale it and make it more enterprisey, upgrade from docker compose to kubernetes. For reference, I'm used to 13B models generating at 2T/s, and 7B models at 4 T/s. 1. 95. ggml. 2-GGUF and the Speechless 13B. Basaran, on the other hand, is built on the HuggingFace ecosystem, allowing you to use the latest open-source models, not just limited to the LLaMA-family. Under Download custom model or LoRA, enter TheBloke/Llama-2-70B-GPTQ. 92 votes, 42 comments. And my GPTQ repo here: alpaca-lora-65B-GPTQ-4bit. Note that the GPTQs will need at least 40GB VRAM, and maybe more. gguf it takes literall seconds. GPTQ is now considered an outdated format. Sabin_Stargem. cpp GGML models, so we can compare to figures people have been doing there for a while. Instead of using GPU and long training times to get a conversation format, you can just use a long system prompt. Now that it works, I can download more new format models. cpp repo, the difference in perplexity between a 16 bit (essentially full precision) 7b model and the 13b This is a video of the new Oobabooga installation. Other than that, the 'best' model is going to depend on what you're Aug 22, 2023 · Software. Also to increase context with it, you only need to pass the -c flag and put in a number like -c 8092 and llama. cpp has made significant performance optimizations for LLaMA variants, but this also limits the range of supported models. 91 ms per token) llama. Oobabooga has been upgraded to be compatible with the latest version of GPTQ-for-LLaMa, which means your llama models will no longer work in 4-bit mode in the new version. Preferably combined with beam search as well. Like the original model, This model has been verified that it also has a translation ability between the following languages, but if you want the translation function for these languages, it is PSA for anyone using those unholy 4x7B Frankenmoes: I'd assumed there were only 8x7B models out there and I didn't account for 4x, so those models fall back on the slower default inference path. GPTQ vs bitsandbytes LLaMA-7B(click me) One way is quantization, which is what the GGML/GPTQ models are. epicfilemcnulty. cpp + cblas on a 4090 system with something like a 7600x-7950x cpu. The GGML (and GGUF, which is slightly improved version) quantization method allows a variety of compression "levels", which is what those suffixes are all about. Hellaswag is just one benchmark and I looked at the examples inside the tests what it's actually asking the models and I think 0-shot testing is a bit brutal for these models. There is an undocumented way to use an external llama. cpp's API and act as a GUI. cpp , koboldcpp , and C Transformers I guess. Because of the different quantizations, you can't do an exact comparison on a given seed. cpp. cpp added a server component, this server is compiled when you run make as usual. In summary, the size reduction in AWQ models is achieved through a novel adaptive quantization method that optimizes the quantization process based on the importance of each weight to the model's performance. !CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python. ago. (1. Hello, I've been trying to offload transformer layers to my GPU using the llama. Llama. q6_K version of the model (llama. This implementation supports the OPT and BLOOM families of LLMs. Their eval shows it's a little weaker than LLaMA-"30B" (which would actually be called 33B if it weren't for a typo in the download), which makes sense, since in the blogpost they note that: MPT-30B trains 30B params on 1T tokens. As always, please read the README! All results below are using llama. Technically yes, but it'll run very slowly. com) I think GGUF don't like multiple GPUs. You can also export quantization parameters with toml+numpy format. cpp, but it can act as such. I've installed the latest version of llama. You can use it as an OpenAI replacement (check out the included `Langchain` example in Let's see, there's: llama. llama. Then there's GGML (but three versions with breaking changes), GPTQ models, GPTJ?, HF models, . The speed discrepancy between llama-cpp-python and llama. The model is taking about 5 -8 min to reply to the example prompt given I liked "Breaking Bad" and "Band of Brothers". On llama. cpp with all layers offloaded to GPU). ***Due to reddit API changes which have broken our registration system fundamental to our security model, we are unable to accept new user registrations until reddit takes satisfactory action. We would like to show you a description here but the site won’t allow us. In that thread, someone asked for tests of speculative decoding for both Exllama v2 and llama. • 7 mo. 1. • 1 yr. Basically, 4-bit quantization and 128 groupsize are recommended. Dang. It ultilizes a calibration dataset to improve quality at the same bitrate. Single RTX 4090 FE at 45 tokens/s but with penalty if running 2 that only 15-20 tokens/s. You could try llama. I normally use LMstudio since i liked the interface but i I am in the middle of some comprehensive GPTQ perplexity analysis - using a method that is 100% comparable to the perplexity scores of llama. If the model is smart enough, it could automatically work to steer that user's thoughts, or to manipulate the user in other ways (for example, sex is a great manipulative tool - a fake female user could start an online relationship with the user, for example, and drive things in potentially dangerous directions). Input Models input text only. You could also try a more aggressively quantized model, but it can hinder performance. GPTQLoRA: Efficient Finetuning of Quantized LLMs with GPTQ. Set max_seq_len to a number greater than 2048. cpp! My brother has an old 8gb system ram + fx 6100 + gtx 750 ti (2gb vram) and this model works incredibly well for him. . MMQ dimensions set to "FAVOR SMALL". bin 3 1` for the Q4_1 size. for example, model_type of WizardLM, vicuna and gpt4all are all llama, hence they are all supported by auto_gptq. But if you want to fine-tune an already quantized model -- yes, it is certainly possible to do on a single GPU. Alternatively, here is the GGML version which you could use with llama. q4_1. You could most likely find a different test set that Falcon-7b would perform better on than Llama-7b. oh and write it in the style of Cormac McCarthy. cpp, only GGML models do. The idea is to create multiple versions of LLaMA-65b, 30b, and 13b [edit: also 7b] models, each with different bit amounts (3bit or 4bit) and groupsize for quantization (128 or 32). 7B-instruct-GPTQ for example), I believe it works without issue. cpp with gpu (sorta if you can figure it out i guess), autogptq, gptq triton, gptq old cuda, and hugging face pipelines. cpp, koboldcpp and tabby have issues with rendering (and therefore stopping) on special tokens for this llama 3 based model This fine tune is based on the Llama 3 8B instruct. Context is hugely important for my setting - the characters require about 1,000 tokens apiece, then there is stuff like the setting and creatures. According to the chart in the llama. cpp examples), but if the model is already struggling, I think grammars can really mess with the output quality. KoboldCPP uses GGML files, it runs on your CPU using RAM -- much slower, but getting enough RAM is much cheaper than getting enough VRAM to hold big models. For ex, `quantize ggml-model-f16. These models are intended to be run with Llama. I also fine-tuned the Llama 3 8B base. They might even join and interact. Variations Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants. Buy, sell, and trade CS:GO items. For instance, use 2 for max_seq_len = 4096, or 4 for max_seq_len = 8192. 5 will work with 7k). There is mention of this on the Oobabooga github repo, and where to get new 4-bit models from. I generally only run models in GPTQ, AWQ or exl2 formats, but was interested in doing the exl2 vs. This adds full GPU acceleration to llama. cpp has been almost fixed. Test them on your system. I've test on chronos-hermes-13B-GPTQ (64g_act), with exllama, I need put a ooc order to make it generate a new random char in a complex card (mongirl from chub. It is now able to fully offload all inference to the GPU. All 3 versions of ggml LLAMA. cpp, from which train-text-from-scratch extracts its vocab embeddings, uses "<s>" and "</s>" for bos and eos, respectively, so I duly encapsulated my training data with them, for example these chat logs: Nov 8, 2023 · The creators of GPTQ, based at the IST Austria Distributed Algorithms and Systems Lab, have made the code publicly available on GitHub. ExLlama doesn't support 8-bit GPTQ models, so llama. The speed was ok on both (13b) and the quality was much better on the "6 bit" GGML. cpp? TheBloke/Llama-2-7b-Chat-GPTQ (the output is not consistent. Sort by: Add a Comment. 0. The model will start downloading. cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box. 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. txt file contents into the main venv. with ```···--alpha_value 2 --max_seq_len 4096···, the later one can handle upto 3072 context, still follow a complex char settings (the mongirl card from chub. 5. The models in that k_quantization_vs_perplexity graph you posted were GGML models, not GPTQ, so they wouldn't be able to use ExLlama, they use llama. I don't think GPTQ works with llama. The thing is llama. It is widely adapted to almost all kinds of model and can be run on may engines. cpp tree) on pytorch FP32 or FP16 versions of the model, if those are originals. On Linux you had the choice to use the triton or cuda branch for GPTQ, but I don't know if that is still the case. A usual Oobabooga installation on windows will use a GPTQ wheel (binary) compiled for cuda/windows, or alternatively use llama. cpp officially supports GPU acceleration. Here's a breakdown: old approach (everybody): if mmq is enabled, just use mmq. Wouldn't call that "Uncensored" to avoid further confusion (there's also a misnamed Llama 2 Chat Uncensored which actually is a Llama 2-based Wizard-Vicuna Unfiltered). gguf. The original ALMA-7B supports English (en) and Russian (ru) translation. You can see the screen captures of the terminal output of both below Update of (1) llama. cpp (with GPU offloading. If you have older hardware give this (specifically WizardLM-7B-uncensored. Take a look at this post about recent auto-gptq In Google Colab, though have access to both CPU and GPU T4 GPU resources for running following code. It is the first "smart" quantization method. Since the same models work on both you can just use both as you see fit. And switching to GPTQ-for-Llama to load the Here is a 4-bit GPTQ version that will work with ExLlama, text-generation-webui etc. cpp/llamacpp_HF, set n_ctx to 4096. Also: Thanks for taking the time to do this. This model supports Japanese (ja) and English translations instead of Russian. bin (or D:\llama2-7b. It's not supported. So now llama. *** Just a guess: you use Windows, and your model is stored in the root directory of your D: drive?. The tuned versions use supervised fine-tuning My recollection is that the example I was quoting in this post was in fact 11t/s on 2x 3090's without nvlink and that jumped to 20 or so when I enabled P2P transfers in llama. 40 tokens/s, 511 tokens, context 2000, seed 1572386444) Just for comparison, I did 20 tokens/s on exllama with 65B. It should be less than 1% for most people's use cases. if you're in the dir directly above the repo, just do the following: GPTQ 4-bit at the model level can shockingly enough improve model performance, though I’m unsure about LoRA’s. cpp to open the API function and run on the server. cpp`. bin, . It supports offloading computation to Nvidia GPU and Metal acceleration for GGML models. You would want to use more like Llama-2-7b--chat-4bit or Llama-2-13b-chat-4bit. 8 tokens per second output and 30-50 tokens per second input. On ExLlama/ExLlama_HF, set max_seq_len to 4096 (or the highest value before you run out of memory). However, the implementation is a lot more efficient than other offloading techniques, like the one Ooba uses to offload from GPU to CPU, so users It is REALLY slow with GPTQ for llama and multiGPU, like painfully slow, and I can't do 4K without waiting minutes for an answer lol Here is the speeds I got at 2048 context Output generated in 212. While parallel community efforts such as GPTQ-for-LLaMa, Exllama and llama. Make sure to use both the GPU and CPU memories. Another couple of options are koboldcpp (GGML) and Auto-GPTQ. Edit added Note: the directory structure means nothing, it’s just a suggested setup. GPTQ: The old and good one. I was not really paying very close attention because I struggled with non-cuda support on the bitsandbytes at the same time and GPTQ working was like an afterthought GPTQ-for-LLaMA has no documentation regarding this and scouring it's source code for how it loads the model has been a pain. cpp discussion. AFAIK, GPTQ models are quantized but can only run on the GPU, and GGML models are quantized but can run on the CPU with llama. I used it with iq4_xs, but get 0. cpp Exllama is for GPTQ files, it replaces AutoGPTQ or GPTQ-for-LLaMa and runs on your graphics card using VRAM. cpp project. cpp-based drop-in replacent for GPT-3. To download from a specific branch, enter for example TheBloke/Llama-2-70B-GPTQ:gptq-4bit-32g-actorder_True; see Provided Files above for the list of branches for each option. bin since Windows usually uses backslash as file path separator). LLaMA-30B trains 32. However, what is the reason I am encounter limitations, the GPU is not being used? I selected T4 from runtime options. It is possible to fine-tune (meaning LoRA or QLoRA methods) even a non quantized model on a RTX 3090 or 4090, up to 34B models. cpp Python binding, but it seems like the model isn't being offloaded to the GPU. Make sure to also set Truncate the prompt up to this length to 4096 under Parameters. cpp ) A good starting point for assessing quality is 7b vs 13b models. I've had good The most excellent JohannesGaessler GPU additions have been officially merged into ggerganov's game changing llama. I'm currently running a rtx 4090 24gb, 3090 24gb, i9-13900, 96gb ram. CPP models (ggml, ggmf, ggjt) All versions of ggml ALPACA models ( legacy format from alpaca. With GGUF models you can load layers onto CPU RAM and VRAM both. llama_print_timings: sample time = 166. These are served, like u/rnosov said, using llama. See the Result drop-down menu on the GPTQ github page, the lowest it goes to is 3 bits. 51 seconds (2. Absolutely stunned. There are also several other implementations that apply GPTQ to Llama models, including the well-known Llama. Also, if you have a powerful macbook, it runs great in LM Studio on OSX. model_type to compare with the table below to check whether the model you use is supported by auto_gptq. You might have some luck with grammars (search for gbnf llama. Set compress_pos_emb to max_seq_len / 2048. ". The only thing I can think of is increasing the batch size. EXL2 is designed for exllamav2, GGUF is made for llama. Of course, I do not want an AI that spouts The minimalist model that comes with llama. Using that, these are my timings after generating a couple of paragraphs of text. 3- Then, use the following command to clean-install the `llama-cpp-python` : pip install --upgrade --force-reinstall --no-cache-dir llama-cpp-python If the installation doesn't work, you can try loading your model directly in `llama. According to GPTQ paper, As the size of the model increases, the difference in performance between FP16 and GPTQ decreases. Usually comes at 3, 4, or 8 bits. If you want less context but better quality, then you can also switch to a 13B GGUF Q5_K_M model and use llama. Exllama and llama. gguf gpt4-x-vicuna-13B. Exllama is GPU only. Run convert-llama-hf-to-gguf. Looks like the Llama 2 13B Base model. You can use llama. cpp, and AWQ is for auto gptq. cpp in CPU mode. GPTQ should be significantly faster in ExLlamaV2 than in V1. Some responses were almost GPT-4 level. with exllama_hf, I don't need that occ order. cpp, llama. Do you have any recommendations of other shows I might like? and using kobold. ### Instruction: write a short three-paragraph story that ties together themes of jealousy, rebirth, sex, along with characters from Harry Potter and Iron Man, and make sure there's a clear moral at the end. cpp? I wonder what speeds someone would get with something like a 3090 + p40 setup. Autogptq was my go-to before but now llama. I'll be posting those this weekend. Again, I'll skip the math, but the gist is JebryyathHS • 2 hr. Output Models generate text and code only. Here is the project link : Cria - Local LLama2 OpenAI compatible API. (TheBloke/deepseek-coder-6. The PR added by Johannes Gaessler has been merged to main Llama. 11 release, so for now you'll have to build from If you wanna try fine-tuning yourself, I would NOT recommend starting with Phi-2 and starting for with something based off llama. cpp recently added support for offloading layers to the GPU. For me, I'm moving more towards prompt chains. Run ollama as one of your docker containers (it's already available as a docker container). cpp (with optional GPU acceleration). Deploy it securely, and you're done. The difference from QLoRA is that GPTQ is used instead of NF4 (Normal Float4) + DQ (Double Quantization) for model quantization. Where I ask simple questions at each step to get the data I need. 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. It rocks. cpp and followed the instructions on GitHub to enable GPU I have a 4070 (12 GB) and a 3090 in the mail. Tried it out. vLLM would probably be the best, but it only works with nvidia cards with a compute capability >= 7. Run quantize (from llama. 44x more FLOPs. cpp to run all layers on the card, you should be able to run at the Autogptq is mostly as fast, it converts things easier and now it will have lora support. cpp and fiddle with the compile-time arguments, but I doubt you'll get much out of that. Yes. Hey there fellow LLaMA enthusiasts! I've been playing around with the GPTQ-for-LLaMa GitHub repo by qwopqwop200 and decided to give quantizing LLaMA models a shot. The length that you will be able to reach will depend on the model size and your GPU memory. ai ), if I change the context to 3272, it failed. However, you will find that most quantized LLMs available online, for instance, on the Hugging Face Hub, were quantized with AutoGPTQ (Apache 2. 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 Running a 3090 and 2700x, I tried the GPTQ-4bit-32g-actorder_True version of a model (Exllama) and the ggmlv3. MMQ dimensions set to "FAVOR BIG" new approach (upstream llama. Click Download. The advantage is that you can expect better performance because it provides better quantization than conventional bitsandbytes. It can be directly used to quantize OPT, BLOOM, or LLaMa, with 4-bit and 3-bit precision. cpp server is giving me many weird issues during inference (If I use chatml template then some prompts will take 10x time to process or not process at all and get stuck) it takes more vram and is slower than gptq/awq/exl2. cpp or KoboldCPP, and will run on pretty much any hardware - CPU, GPU, or a combo of both. cpp): you cannot toggle mmq anymore. I've heard the latest llama. Takes 2 minutes to load the model then another minute to make a full response to your question. 5B params on 1. cpp implement quantization methods strictly for the Llama architecture, AutoGPTQ gained popularity through its smooth coverage of a wide range of transformer architectures. I feel that the most efficient is the original code llama. The reason ,I am not sure. Some q4_0 results: Below is an instruction that describes a task. If you really wanna use Phi-2, you can use the URIAL method. If you can successfully load models with `BLAS=1`, then the issue might be with `llama-cpp-python`. 57 (4 threads, 60 layers offloaded) on a 4090, GPTQ is significantly faster. Keep in mind, this is gpt-2, so the output quality is going to be pretty poor. OpenLLM leaderboard, I expected it to not be the same but I can't explain why it doesn't match with the llama. This is faster than running the Web Ui directly. Takes a lot time and vram+ram to make a GPTQ quant. You should be able to run gguf. Model Architecture Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. Could you help me understand the deep discrepancy between resource usage results from vllm vs. ExLLaMA is a loader specifically for the GPTQ format, which operates on GPU. 7, top_k=40, top_p=0. cpp comparison. Most people would agree there is a significant improvement between a 7b model (LLaMA will be used as the reference) and a 13b model. And I've seen a lot of people claiming much faster GPTQ performance than I get, too. Run your website server as another docker container. Llama 2 Airoboros 7/13/70B GPTQ/GGML Released! Find them on TheBloke's huggingface page! Hopefully, the L2-70b GGML is an 16k edition, with an Airoboros 2. 4T tokens. If you intend to perform inference only on CPU, your options would be limited to a few libraries that support the ggml format, such as llama. Here are some examples. It is always enabled. GPTQ's official repository is on GitHub (Apache 2. cpp's speed on GPU is faster than GPTQ is for me with newer releases (and many times on par with or better than Exllama). LLaMA models can't be quantized to 2-bit via GPTQ. So you'll need 2 x 24GB cards, or an A100. com) posted by TheBloke. Even setting very extreme sampler settings seems to have mostly no effect, whereas GPTQ becomes unintelligible, as it should be in my experience. This is a super interesting question Reply reply More replies For GPTQ in Exllama1 you can run a 13B Q4 32g act_order true, then use RoPE scaling to get up to 7k context (alpha=2 will be ok up to 6k, alpha=2. I finished the set-up after some googling. Using CPU alone, I get 4 tokens/second. cpp just got full CUDA acceleration, and now it can outperform GPTQ!: LocalLLaMA (reddit. ) I got GPTQ working. The tests were run on my 2x 4090, 13900K, DDR5 system. cpp provides a converter script for turning safetensors into GGUF. Besides the specific item, we've published initial tutorials on several topics over the past month: Building instructions for discrete GPUs (AMD, NV, Intel) as well as for MacBooks Aug 23, 2023 · you can use model. 2. It won't use both gpus and will be slow but you will be able try the model. This is different from running the entire model on the GPU like GPTQ does because some of the computation is still done on the CPU. Grammar is extremely useful tho, which is why I have to use llama. For now you should use Aphrodite or vLLM as a backend -- llama. - relevant part of the source code. But for me, using Oobabooga branch of GPTQ-for-LLaMA AutoGPTQ versus llama-cpp-python 0. cpp It was an an X99 motherboard (GA-X99 I believe), a the standard 4-slot nvlink bridge, 2x TUF 3090's, 2699v4 cpu, 128GB ram, running on the latest version of arch linux This makes running 13b in 8-bit precision the best option for those with 24GB GPUs. cpp (for GGML models) and exllama (GPTQ). Resources. Reply reply. 1-GGUF (so far this is the only one that gives the output consistently. If you have an Nvidia GPU and want to use the latest llama-cpp-python in your webui, you can use these two commands: I have been working on an OpenAI-compatible API for serving LLAMA-2 models written entirely in Rust. Open the Model tab, set the loader as ExLlama or ExLlama_HF. cpp) models and run them at 15-10 tokens/s (depending if the context is filled or not, and the amount of quantization). cpp / gpt-llama/ chatbot-ui stack and find it works well, but it wasn’t super easy to set up the first time. Oobabooga isn't a wrapper for llama. pre_layer is set to 50. cpp offers a variety of quantizations I don't understand what method do they utilize? Others have proper resources or research papers on their methods and their effectiveness but couldn't find the same for llama. It's not supported but the implementation should be possible, technically. Sometimes I get an empty response or without the correct answer option and an explanation data) TheBloke/Llama-2-13b-Chat-GPTQ (even 7b is better) TheBloke/Mistral-7B-Instruct-v0. Should still be cheaper than a 4090, but what I'm curious about is if that combination would be faster than running the ggml version of the same 65b model with llama. 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). I can squeeze in 38 out of 40 layers using the OpenCL enabled version of llama. If cublas is enabled, just use cublas. I have an rtx 3060 laptop with 16gb of ram. 0 License). Your work is greatly appreciated. Hey all, I had a goal today to set-up wizard-2-13b (the llama-2 based one) as my primary assistant for my daily coding tasks. cpp tree) on the output of #1, for the sizes you want. On a 7B 8-bit model I get 20 tokens/second on my old 2070. 5-2 t/s with 6700xt (12 GB) running WizardLM Uncensored 30B. Once it's finished it will say "Done". They converg to a similar eval loss: Most methods like GPTQ OR AWQ use 4-bit quantization with some keeping salient weights in a higher precision. EXL2 is the fastest, followed by GPTQ through ExLlama v1 This is a little surprising to me. I use llama. I can not tell the diffrence of text between TheBloke/llama-2-13B-Guanaco-QLoRA-GPTQ with chronos-hermes-13B-GPTQ, except a few things. ai), otherwise it always generate the same char as example chats. Transformers has the load_in_8bit option, but it's very slow and unoptimized in comparison to load_in_4bit. There's an update now that enables the fused kernels for 4x models as well, but it isn't in the 0. Any help appreciated EDIT: SOLVED! after some time getting my head in the GPTQ-For-LLaMA i got how it loaded the models. 0 dataset. config. Q5_K_M. This 13B model was generating around 11tokens/s. (For context, I was looking at switching over to the new bitsandbytes 4bit, and was under the impression that it was compatible with GPTQ, but…. Here's my data point: With the 12 GB of the 4070 I can fit 13B Q6_K_M or Q5_K_M GGUF (llama. bg yf fx ii zl rn uj gn vg ob