Exllama hf reddit github. Supports transformers, GPTQ, AWQ, EXL2, llama.

Exllama hf reddit github Supports transformers, GPTQ, AWQ, EXL2, llama. Before that, we need to copy essential config files from the base_modeldirectory to the new quant directory. Contribute to Yiyi-philosophy/hf_llama development by creating an account on GitHub. dev. I would dare to say, is one of the biggest jumps on the LLM scene recently. The 32 refers to my A6000 (the first GPU ID set in the environment variable CUDA_VISIBLE_DEVICES), so I don't pre-load it to its max 48GB. On a git console, or powershell (or bash on linux) on the textgen folder, git fetch origin pull/2955/head:ntkropepr git checkout ntkropepr Then, if ooba merges it, or you want to revert, you just can do: git checkout main Should work with exllama_hf too. Classifier-Free Guidance is now implemented for ExLlama_HF and llamacpp_HF. upvotes /r/StableDiffusion is back open after the protest of Reddit killing open 8000 ctx vs 2000 ctx is a way higher jump vs exllama_hf/exllama. When I use an older backup installation (git show returns "19 December 2023"), my settings and models work normally. In order to bootstrap the process for this example while still building a useful model, we make use of the StackExchange dataset. 122 votes, 79 comments. LocalAI has recently been updated with an example that integrates a self-hosted version of OpenAI's API with a Copilot alternative called Continue. After that, load them using the "ExLlama_HF" loader. You can offload inactive users' caches to system memory (i. env file if using docker compose, or the I'm developing AI assistant for fiction writer. KoboldCPP uses GGML files, it runs on your CPU using RAM -- much ExLlama_HF uses the logits from ExLlama but replaces ExLlama's sampler with the same HF pipeline used by other implementations, so that sampling parameters are interpreted the same and more samplers are The official and recommended backend server for ExLlamaV2 is TabbyAPI, which provides an OpenAI-compatible API for local or remote inference, with extended features like HF model There's an update now that enables the fused kernels for 4x models as well, but it isn't in the 0. py script, it did convert the lora into GGML format, but when I tried to run a GGML model with this lora, lamacpp just segfaulted. There's a lot of debate about using GGML, or GPTQ, AWQ, EXL2 etc performance etc. I don't know if manually splitting the GPUs is needed. - gabyang/textgen-webui Last time I've tried it, using their convert-lora-to-ggml. image, and links to the exllama topic page so that developers can more easily learn about it. It's still the full-sized model that chooses tokens. Try to load a model which can't be used on same GPU, but in more than 1 GPUs. It is also possible to run the 13B model using llama. You switched accounts on another tab or window. cpp_HF wrapper that is also functional for evaluation. So the CPU bottleneck is removed, and all HF loaders are now faster, including ExLlama_HF and ExLlamav2_HF. Subreddit to discuss about Llama, the large language model created by Meta AI. Load exllama_hf on webui. As openai API gets pretty expensive with all the inference tricks needed, I'm looking for a good local alternative for most of inference, saving gpt4 just for polishing final results. 169K subscribers in the LocalLLaMA community. Now that our model is quantized, we want to run it to see how it performs. I was having a similar issue before with it taking a lot of RAM, are you using exllama or exllama_hf as the loader? If so, it's not supposed to use over a few gigabytes ever, make sure your Oobabooga installation is updated. You signed in with another tab or window. ) Can you describe how you experience the difference? ExLlama gets around the problem by reordering rows at load-time and discarding the group index. I've been getting gibberish responses with exllama 2_hf. I've been doing more tests, and here are some MMLU scores to compare. Personally, Ive had much better performance with GPTQ (4Bit and group size of 32G gives massively better quality of result than the 128G models). 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. It's already kind of unwieldy. The dataset includes questions and their corresponding answers from the StackExchange platform (including StackOverflow for code and many other topics). 7 tokens/s after a few times regenerating. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects. It reads HF models but doesn't rely on the framework. exlla A fast inference library for running LLMs locally on modern consumer-class GPUs - exllamav2/README. But it was a while ago, probably that has been fixed already. 2OP: exllama supports loras, so another option is to convert the base model you used for fine-tuning into GPTQ format, and then use it with The subreddit for all things related to Modded Minecraft for Minecraft Java Edition --- This subreddit was originally created for discussion around the FTB launcher and its modpacks but has since grown to encompass all aspects of modding the Java edition of Minecraft. If I built out ExLlama every time someone had an interesting idea on reddit it'd be an unmaintainable behemoth by now. NOTE: by default, the service inside the docker container is run by a non-root user. Curate this topic Add this topic to your repo To associate your repository with If you only need to serve 10 of those 50 users at once, then yes, you can allocate entries in the batch to a queue of incoming requests. Skip to content. It is now about as fast as using llama. For me, these were the parameters that worked with 24GB VRAM: ExLLaMA is a loader specifically for the GPTQ format, which operates on GPU. cpp by sending part of the layers to the GPU. For that, download the q4_K_M file manually (it's a single file), put it into text-generation-webui/models, and load it with the "llama. View community ranking In the Top 5% of largest communities on Reddit. You may have to reduce max_seq_len if you run out of memory while trying to generate text. Reload to refresh your session. A Gradio web UI for Large Language Models. log of perplexity) and I can confirm that it works in exllama_hf (as described in this As for the "usual" Python/HF setup, ExLlama is kind of an attempt to get away from Hugging Face. I tried to isolate the issue - its not SillyTavern, its not my I'm having a similar experience on an RTX-3090 on Windows 11 / WSL. 5, you have a pretty solid alternative to GitHub Copilot that runs There’s an excellent guide on the Exllamav2 GitHub: With the fused attention it is fast like exllama, but without it is slow AF. cpp , koboldcpp , and C Transformers I guess. I've been meaning to write more documentation and maybe even a tutorial, but in the meantime there are those examples, the project itself, and a lot of other projects using it. (I'm still in GPTQ-land w/ TGWUI & exllama/exllama_hf from about a month or two ago. cpp directly, but with the following benefits: More samplers. md at master · turboderp/exllamav2 View community ranking In the Top 10% of largest communities on Reddit. Gathering human feedback is a complex and expensive endeavor. . The issue is that you can only start generating the token at position n when you've already decided on the token at position n Upvote for exllama. Hence, the ownership of bind-mounted directories (/data/model and /data/exllama_sessions in the default docker-compose. Well, there is definitely some loss going from 5 bits (or 5. I recently switched from exllama to exllama_hf because there's a bug that prevents the stopping_strings param from working via the API, and there's a branch on text-generation-webui that supports stopping_strings if you use exllama. While they track pretty well with perplexity, there's of course still more to the story, like potential stability issues with lower bitrates that might not manifest until you really push the model out of its In the Model tab, select "ExLlama_HF" under "Model loader", set max_seq_len to 8192, and set compress_pos_emb to 4. Weirdly, inference seems to speed up over time. A post about exllama_hf would be interesting. 0. Even after the arena that ooba did, the most used settings are already being used on exllama itself (top p, top k, typical and rep penalty). long_seq for llama. Also the memory use isn't good. They're in the test branch for now, since I need to confirm that they don't break anything (on ROCm in 1) Make ExLlama_HF functional for evaluation. I saw this post: #2912 But I'm a newbie, and I have no idea what half 2 is or where to go to disable it. 11 release, so for now you'll have to build from source to get full speed for those. Each of these took more hours to get working than I am willing to Recent Highlights from the Previous Thread: >>103699668--Bots scraping content and potential countermeasures: >103700478 >103700544 >103700558 >103700570 🦙 Running ExLlamaV2 for Inference. 4 bits. Load a model shared between 2 GPUs. turboderp/exllama#118 This hasn't been vetted/merged yet but in practice, it seems to unlock the context of un-finetuned models based on the scaling alpha value and does it with minimal perplexity loss. Get all the model loaded in GPU 0; For the second issue: Apply the PR Fix Multi-GPU not working on exllama_hf #2803 to fix loading in just 1 GPU. 👍 2 Panchovix and alkeryn reacted with thumbs up emoji This seems super weird, I'm not sure what he's trying to do just comparing perplexity and not accounting for file size, performance, etc. From a quick glance at the github, tau reperesent the average surprise value (i. e. This issue caused some people to opportunistically claim that the webui is I'd be very curious about the tokens/sec you're getting with exllama or exllama_hf loaders for typical Q/A (small) and long-form chat (large) contexts (say, 200-300 tokens and 1800-2000 Exllama is for GPTQ files, it replaces AutoGPTQ or GPTQ-for-LLaMa and runs on your graphics card using VRAM. On a 70b parameter model with ~1024 max_sequence_length, repeated generation starts at ~1 tokens/s, and then will go up to 7. To disable this, set RUN_UID=0 in the . 2) Create a llama. cpp" loader: Eval mmlu result against various infer methods (HF_Causal, VLLM, AutoGPTQ, AutoGPTQ-exllama) Discussion I modified declare-lab's instruct-eval scripts, add support to VLLM, AutoGPTQ (and new autoGPTQ support exllama now), and test the mmlu result. while they're still reading the last reply or typing), and you can also use dynamic batching to make better use of VRAM, since not all users will need the full context all the time. Any reference for how much VRAM each bit version takes? I've made some changes to the GPTQ kernel to increase precision. Basically, we want This is done with the llamacpp_HF wrapper, which I have finally managed to optimize (spoiler: it was a one line change). It seems like it's mostly between 4-bit-ish quantizations but it doesn't actually say that. Then, select the llama-13b-4bit-128g model in the "Model" dropdown to load it. It's obviously a work in progress but it's a fantastic project and wicked fast 👍 Because the user-oriented side is straight python is much easier to script and you can just read the code to understand what's going on. turboderp's ExLlama2_HF is pretty excellent with using as little memory as possible (I think over AutoGPTQ youre getting around a 5-15% lowered memory use on VRAM). Logs GitHub is where people build software. You signed out in another tab or window. comments sorted by Best Top New Controversial Q&A Add a Speculative decoding doesn't affect the quality of the output. Try to do inference. cpp (GGUF), Llama models. If you pair this with the latest WizardCoder models, which have a fairly better performance than the standard Salesforce Codegen2 and Codegen2. Minor thing, but worth noting. github. Screenshot. yml file) is changed to this non-root user in the container entrypoint (entrypoint. AutoGPTQ and GPTQ-for-LLaMA don't have this optimization (yet) so you end up paying a big performance penalty when using both act-order and group size. sh). 5 or whatever Q5 equates to) down to 2. qchp nlpjx ruffhj ikk xaeau awmxg epixcik qrctta zcnt xxitass