troyanovsky / local-llm-comparison-colab-ui Goto Github PK

Compare the performance of different LLM that can be deployed locally on consumer hardware. Run yourself with Colab WebUI.

Jupyter Notebook 100.00%

ai gpt llama llm

local-llm-comparison-colab-ui's Introduction

👋 Hi, I’m @Troyanovsky
👀 I’m a product manager during my day, but I often code some small projects while learning new stuff in my spare time.
🌱 I’m currently learning Large Language Model applications and Web dev.
💞️ I’m looking to collaborate on projects in EdTech, HealthTech, Productivity Tools, or anything that could make people's lives better! These projects can be just MVP/Proof-of-concept to bigger ones.
📫 How to reach me: [email protected]; https://www.linkedin.com/in/guodongzhao/

local-llm-comparison-colab-ui's People

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seshakiran rozek tuananhnguyenkim decentralised-ai groundbasesoft wsuff luisriverag wsue gavinchen1314 ramstorage tranhoangnguyen03 ai-jie01 ai-ar4s-dev erima2020 bitsnaps gary109 tnguyenqh jasoncaojingren afilimonov arlvr andycar jcarlosneto glaceage redaalarbi sksundaram-learning krishna999 beachhost lolmeup abhishekrai43 say383 vcappuccio shashwatvt blackwhites ilabutk klipski alienfacepalm kuntal-c emanuelriquelme capzulaz3 proalf ronbalt baileypillon girijeshcse fedaykinofdune bimal-mehta techthiyanes soheilkhodayari narcisoperez bmedi mustafaali96 nissan jakecho lutfimuh lpgpa miles-canton octag0no yasamnik caesarchad silasdao gitrjaa meril99 somesh24 tiamat-tech teddyseptiadi goldendawn369 kp-forks evdcush mainmasterdev hertera1 minyang-chen meta-ks patbhakta vtaranti supun156 syaikhipin igeralt abduibasit 13alvone codenfish cahlen ziyanda-tembe chorseng davidgao7 mkotlarz gumus-helio novagaming1964 kendogg09 cybermazinho f901107 dolife trombone1 heyhimansh tundachef stophobia cheehoon-lee silverdevelopper finyellow majidabdelilah zyh1994 kristofleroux

local-llm-comparison-colab-ui's Issues

ImportError: libcudart.so.12

when I tried to run Mistral-7B-OpenOrca (using oobabooga/text-generation-webui)Ж
ImportError: libcudart.so.12: cannot open shared object file: No such file or directory

Which runs on least powerful hardware...

Do you have any data/sorting of which ones used the least amount of resources? Versus which did the best. The usecase I have in mind is in a very constrained server environment, so something that doesn't use much memory or CPU would be a key driver in picking one, versus which does best. I'm thinking in the 2 GB of ram space....

Feature: add new model - Nous-Hermes-13b

https://huggingface.co/NousResearch/Nous-Hermes-13b
https://twitter.com/Teknium1/status/1664844441742618629

CUDA driver version is insufficient for CUDA runtime version

This only happen with the WizardLM-1.0-Uncensored-Llama2-13B on Google colab
Thank you for taking a look

/content
Selecting previously unselected package libc-ares2:amd64.
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Cloning into 'text-generation-webui'...
remote: Enumerating objects: 15569, done.
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Note: switching to 'ebb4f22028316821c3c14ad91b45e01d309f1ff0'.

You are in 'detached HEAD' state. You can look around, make experimental
changes and commit them, and you can discard any commits you make in this
state without impacting any branches by switching back to a branch.

If you want to create a new branch to retain commits you create, you may
do so (now or later) by using -c with the switch command. Example:

  git switch -c <new-branch-name>

Or undo this operation with:

  git switch -

Turn off this advice by setting config variable advice.detachedHead to false

/content/text-generation-webui
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INFO: pip is looking at multiple versions of mdit-py-plugins to determine which version is compatible with other requirements. This could take a while.
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INFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. See https://pip.pypa.io/warnings/backtracking for guidance. If you want to abort this run, press Ctrl + C.
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INFO: pip is looking at multiple versions of multiprocess to determine which version is compatible with other requirements. This could take a while.
Collecting multiprocess (from datasets->-r requirements.txt (line 3))
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Collecting smmap<6,>=3.0.1 (from gitdb<5,>=4.0.1->GitPython!=3.1.29,>=1.0.0->wandb->-r requirements.txt (line 20))
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Building wheels for collected packages: llama-cpp-python, peft, ffmpy
  Building wheel for llama-cpp-python (pyproject.toml) ... done
  Created wheel for llama-cpp-python: filename=llama_cpp_python-0.1.77-cp310-cp310-linux_x86_64.whl size=276102 sha256=d4a18f25b6865bde63e3fadcc257c17f27f16301b63fba4f43071e3b981074e5
  Stored in directory: /root/.cache/pip/wheels/aa/ed/39/87f2ad350dbbf13b600ac744899186b8647c5323c62e2bb348
  Building wheel for peft (pyproject.toml) ... done
  Created wheel for peft: filename=peft-0.5.0.dev0-py3-none-any.whl size=73123 sha256=9293bbbd4cea4b825fdad6dc5838818a3a3dde4fa31fd96f1e90ab8013f01f74
  Stored in directory: /root/.cache/pip/wheels/ff/57/c1/a023c490307cd8ffa3b61c86c48d9767f0bb850053af18674b
  Building wheel for ffmpy (setup.py) ... done
  Created wheel for ffmpy: filename=ffmpy-0.3.1-py3-none-any.whl size=5579 sha256=d29996ef42844f560f617ef83521d97f77515d71424168d7537adaa35754fa33
  Stored in directory: /root/.cache/pip/wheels/01/a6/d1/1c0828c304a4283b2c1639a09ad86f83d7c487ef34c6b4a1bf
Successfully built llama-cpp-python peft ffmpy
Installing collected packages: tokenizers, safetensors, pydub, ffmpy, bitsandbytes, websockets, smmap, setproctitle, sentry-sdk, semantic-version, rouge, python-multipart, Pillow, orjson, markdown-it-py, llama-cpp-python-cuda, llama-cpp-python, h11, einops, docker-pycreds, dill, colorama, aiofiles, uvicorn, starlette, multiprocess, mdit-py-plugins, httpcore, gitdb, transformers, httpx, GitPython, fastapi, exllama, accelerate, wandb, peft, gradio_client, datasets, gradio, auto-gptq
  Attempting uninstall: tokenizers
    Found existing installation: tokenizers 0.15.1
    Uninstalling tokenizers-0.15.1:
      Successfully uninstalled tokenizers-0.15.1
  Attempting uninstall: safetensors
    Found existing installation: safetensors 0.4.2
    Uninstalling safetensors-0.4.2:
      Successfully uninstalled safetensors-0.4.2
  Attempting uninstall: Pillow
    Found existing installation: Pillow 9.4.0
    Uninstalling Pillow-9.4.0:
      Successfully uninstalled Pillow-9.4.0
  Attempting uninstall: markdown-it-py
    Found existing installation: markdown-it-py 3.0.0
    Uninstalling markdown-it-py-3.0.0:
      Successfully uninstalled markdown-it-py-3.0.0
  Attempting uninstall: mdit-py-plugins
    Found existing installation: mdit-py-plugins 0.4.0
    Uninstalling mdit-py-plugins-0.4.0:
      Successfully uninstalled mdit-py-plugins-0.4.0
  Attempting uninstall: transformers
    Found existing installation: transformers 4.35.2
    Uninstalling transformers-4.35.2:
      Successfully uninstalled transformers-4.35.2
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
lida 0.0.10 requires kaleido, which is not installed.
imageio 2.31.6 requires pillow<10.1.0,>=8.3.2, but you have pillow 10.2.0 which is incompatible.
Successfully installed GitPython-3.1.41 Pillow-10.2.0 accelerate-0.21.0 aiofiles-23.2.1 auto-gptq-0.3.0+cu117 bitsandbytes-0.41.0 colorama-0.4.6 datasets-2.16.1 dill-0.3.7 docker-pycreds-0.4.0 einops-0.7.0 exllama-0.0.9+cu117 fastapi-0.95.2 ffmpy-0.3.1 gitdb-4.0.11 gradio-3.33.1 gradio_client-0.2.5 h11-0.14.0 httpcore-1.0.2 httpx-0.26.0 llama-cpp-python-0.1.77 llama-cpp-python-cuda-0.1.77+cu117 markdown-it-py-2.2.0 mdit-py-plugins-0.3.3 multiprocess-0.70.15 orjson-3.9.12 peft-0.5.0.dev0 pydub-0.25.1 python-multipart-0.0.6 rouge-1.0.1 safetensors-0.3.1 semantic-version-2.10.0 sentry-sdk-1.40.0 setproctitle-1.3.3 smmap-5.0.1 starlette-0.27.0 tokenizers-0.13.3 transformers-4.31.0 uvicorn-0.27.0.post1 wandb-0.16.2 websockets-12.0
WARNING: The following packages were previously imported in this runtime:
  [PIL]
You must restart the runtime in order to use newly installed versions.
Collecting gradio==3.32.0
  Downloading gradio-3.32.0-py3-none-any.whl (19.9 MB)
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Installing collected packages: gradio
  Attempting uninstall: gradio
    Found existing installation: gradio 3.33.1
    Uninstalling gradio-3.33.1:
      Successfully uninstalled gradio-3.33.1
Successfully installed gradio-3.32.0
Found existing installation: llama_cpp_python 0.1.77
Uninstalling llama_cpp_python-0.1.77:
  Successfully uninstalled llama_cpp_python-0.1.77
Collecting llama-cpp-python==0.1.78
  Downloading llama_cpp_python-0.1.78.tar.gz (1.7 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.7/1.7 MB 9.9 MB/s eta 0:00:00
  Installing build dependencies ... done
  Getting requirements to build wheel ... done
  Preparing metadata (pyproject.toml) ... done
Requirement already satisfied: typing-extensions>=4.5.0 in /usr/local/lib/python3.10/dist-packages (from llama-cpp-python==0.1.78) (4.5.0)
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Building wheels for collected packages: llama-cpp-python
  Building wheel for llama-cpp-python (pyproject.toml) ... done
  Created wheel for llama-cpp-python: filename=llama_cpp_python-0.1.78-cp310-cp310-linux_x86_64.whl size=5811091 sha256=209d670a27a6691b40380caee617af17d99b896bfef56b64fabfce5f603fd2db
  Stored in directory: /tmp/pip-ephem-wheel-cache-rhs2coaq/wheels/61/f9/20/9ca660a9d3f2a47e44217059409478865948b5c8a1cba70030
Successfully built llama-cpp-python
Installing collected packages: llama-cpp-python
Successfully installed llama-cpp-python-0.1.78

Download Results:
gid   |stat|avg speed  |path/URI
======+====+===========+=======================================================
f1cf97|OK  |   164MiB/s|/content/text-generation-webui/models//wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q5_K_M.bin

Status Legend:
(OK):download completed.
/content/text-generation-webui
2024-02-03 08:22:25 WARNING:The gradio "share link" feature uses a proprietary executable to create a reverse tunnel. Use it with care.
/usr/local/lib/python3.10/dist-packages/bitsandbytes/cextension.py:34: UserWarning: The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.
  warn("The installed version of bitsandbytes was compiled without GPU support. "
/usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cpu.so: undefined symbol: cadam32bit_grad_fp32
2024-02-03 08:22:29.859509: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-02-03 08:22:29.859583: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-02-03 08:22:29.861007: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-02-03 08:22:31.839727: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-02-03 08:22:33 INFO:Loading wizardlm-1.0-uncensored-llama2-13b.ggmlv3.q5_K_M.bin...
CUDA error 35 at /tmp/pip-install-nydaa5se/llama-cpp-python_720d0f2868d94d788fd2ec34324b390a/vendor/llama.cpp/ggml-cuda.cu:4883: CUDA driver version is insufficient for CUDA runtime version

Error executing "wizard-vicuna-13B.ggml.q4_0 (using llama.cpp)" on Colab

Hi, I am getting this error whentriying to execute "wizard-vicuna-13B.ggml.q4_0 (using llama.cpp)" :

aria2 will resume download if the transfer is restarted.
If there are any errors, then see the log file. See '-l' option in help/man page for details.
/content/text-generation-webui
2023-10-05 19:35:57 WARNING:The gradio "share link" feature uses a proprietary executable to create a reverse tunnel. Use it with care.
2023-10-05 19:36:00.774040: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
bin /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so
2023-10-05 19:36:05 INFO:Loading wizard-vicuna-13B-GPTQ...
╭───────────────────── Traceback (most recent call last) ──────────────────────╮
│ /content/text-generation-webui/server.py:1017 in <module>                    │
│                                                                              │
│   1014 │   │   update_model_parameters(model_settings, initial=True)  # hija │
│   1015 │   │                                                                 │
│   1016 │   │   # Load the model                                              │
│ ❱ 1017 │   │   shared.model, shared.tokenizer = load_model(shared.model_name │
│   1018 │   │   if shared.args.lora:                                          │
│   1019 │   │   │   add_lora_to_model(shared.args.lora)                       │
│   1020                                                                       │
│                                                                              │
│ /content/text-generation-webui/modules/models.py:74 in load_model            │
│                                                                              │
│    71 │   │   │   │   return None, None                                      │
│    72 │                                                                      │
│    73 │   shared.args.loader = loader                                        │
│ ❱  74 │   output = load_func_map[loader](model_name)                         │
│    75 │   if type(output) is tuple:                                          │
│    76 │   │   model, tokenizer = output                                      │
│    77 │   else:                                                              │
│                                                                              │
│ /content/text-generation-webui/modules/models.py:293 in ExLlama_HF_loader    │
│                                                                              │
│   290 def ExLlama_HF_loader(model_name):                                     │
│   291 │   from modules.exllama_hf import ExllamaHF                           │
│   292 │                                                                      │
│ ❱ 293 │   return ExllamaHF.from_pretrained(model_name)                       │
│   294                                                                        │
│   295                                                                        │
│   296 def get_max_memory_dict():                                             │
│                                                                              │
│ /content/text-generation-webui/modules/exllama_hf.py:92 in from_pretrained   │
│                                                                              │
│    89 │   │   │   if len(found) > 0:                                         │
│    90 │   │   │   │   weight_path = found[-1]                                │
│    91 │   │   │   │   break                                                  │
│ ❱  92 │   │   assert weight_path is not None, f'could not find weight in "{p │
│    93 │   │                                                                  │
│    94 │   │   config.model_path = str(weight_path)                           │
│    95 │   │   config.max_seq_len = shared.args.max_seq_len                   │
╰──────────────────────────────────────────────────────────────────────────────╯
AssertionError: could not find weight in "models/wizard-vicuna-13B-GPTQ"

Try it here: https://docs.litellm.ai/docs/simple_proxy
https://github.com/BerriAI/litellm

Using LiteLLM Proxy Server

Creating a proxy server

Ollama models

$ litellm --model ollama/llama2 --api_base http://localhost:11434

Hugging Face Models

$ export HUGGINGFACE_API_KEY=my-api-key #[OPTIONAL]
$ litellm --model claude-instant-1

Anthropic

$ export ANTHROPIC_API_KEY=my-api-key
$ litellm --model claude-instant-1

Palm

$ export PALM_API_KEY=my-palm-key
$ litellm --model palm/chat-bison

Using to run an eval on lm harness:

python3 -m lm_eval \
  --model openai-completions \
  --model_args engine=davinci \
  --task crows_pairs_english_age

Legal

Fantastic work!
Do you know of models or LORA trained on US law? Sort of lawyer LLM.

make coding scores based on unit tests

Hey,

great initiative to track local llms!

Would you be open to talking about how the scores are created?

I created some gpt-4 scores in a project in the past and found them not good enough (they would fluctuate based on input sentences with the same meanings, scores somewhat too arbitrary, different days would give different scores for the same input). At least you should pin the gpt-4 version so you have better control when they roll updates to gpt-4
For code one could add unit tests to check the created functions

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