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workerbee's Introduction

GPUtopia Workerbee

How to use the worker:

  • first set up an account at gputopia.ai, this is the easiest way to ensure your funds are swept correctly.
  • for now, we only support alby logins. i know this isn't ideal. but it's easier for now. in the future, any ln-wallet should work to log in and claim control over a given lnurl.
  • download or build a release, stick it somewhere nice (/usr/bin/gputopia-worker)
  • from the command-line try this: gputopia-worker --test_model TheBloke/CodeLlama-7B-Instruct-GGUF:Q4_K_M, maybe paste the results into a discord channel for fun and discussion
  • if that works, run gputopia-worker --ln_address your-ln@address-goes-here

Worker command line options:

usage: gputopia-worker [-h] [--auth_key AUTH_KEY] [--queen_url QUEEN_URL] [--ln_address LN_URL] [--loops LOOPS] [--debug]
                            [--test_model TEST_MODEL] [--test_max_tokens TEST_MAX_TOKENS] 
                            [--main_gpu MAIN_GPU] [--tensor_split TENSOR_SPLIT] [--force_layers FORCE_LAYERS]
                            [--layer_offset LAYER_OFFSET] [--version]

options:
  -h, --help                          show this help message and exit
  --version                           output version and exit
  --auth_key AUTH_KEY                 access_token for account login
  --queen_url QUEEN_URL               coordinator url (wss://queenbee.gputopia.ai/worker)
  --ln_address LN_ADDRESS             lightning address ([email protected])
  --loops LOOPS                       quit after getting this number of jobs
  --debug                             verbose debugging info
  --test_model TEST_MODEL             specify a HF_REPO/PATH[:FILTER?] to test
  --test_max_tokens TEST_MAX_TOKENS   more == longer test
  --main_gpu MAIN_GPU                 default "0"
  --tensor_split TENSOR_SPLIT         default "even split", specify comma-delimited list of numbers
  --force_layers FORCE_LAYERS         default, guess layers based on model size
  --layer_offset LAYER_OFFSET         default "2" (fudge guess down by 2, leaving more room for context)

How to build the worker from source:

When building, please ensure you have CUDA installed or OPENCL (for AMD chips). You can also do a METAL build for OSX.

CUDA/NVIDIA build

CMAKE_ARGS="-DLLAMA_CUBLAS=1" FORCE_CMAKE=1 poetry install --with onnx

OSX/METAL build:

CMAKE_ARGS="-DLLAMA_METAL=1" FORCE_CMAKE=1 poetry install --with onnx

if you want it to see the gpus!

CLBLAST build:

get (or build) this:

https://github.com/KhronosGroup/OpenCL-SDK/releases

put it in c:/opencl-sdk or (on linux) cmake --install it

git clone https://github.com/CNugteren/CLBlast.git
mkdir CLBlast/build
cd CLBlast/build
cmake .. -DOPENCL_ROOT=C:/OpenCL-SDK -G "Visual Studio 17 2022" -A x64
cmake --build . --config Release
cmake --install . --prefix C:/CLBlast

CMAKE_ARGS="-DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=C:/CLBlast/lib/cmake/CLBlast" FORCE_CMAKE=1 poetry install --with onnx

Run a dev-mode worker

  • poetry run gputopia_worker

Run a re-quantization on a gguf

  • poetry run quantize_gguf

Run tests to be sure it really works

PYTHONPATH=. pytest tests/

Build your own EXE

pyinstaller --onefile --name gputopia-worker --additional-hooks-dir=./hooks ai_worker/__main__.py

workerbee's People

Contributors

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Stargazers

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Watchers

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Forkers

wilfredallyn bolt-observer lnconsole ritikkhulbe connor-bit-power trigger67 gonzafirewall

workerbee's Issues

Change fine-tune to use lamma-cpp, after getting llama-cpp to use the GPU for fine tuning

this is a big task, but it's important.

pytorch ecosystem is "dependency hell" at best, and rarely works well on other platforms besides linux, especially for tasks with many deps like peft, bitsandbytes

llama cpp ecosystem uses CMAKE and is easy to get to work with linux, windows, max, and even WASM!

we're using pytorch for fine tuning only because llama-cpp doesn't support GPU

the same will apply to stable-diffusion too!

this ticket is for

  • updating llama-cpp downstream to support the gpu
  • updating worker to ditch pytorch and still support the same fine-tune job flow, status updates
  • ok for the lora output to be gguf of course, not pth

error -5 when loading model - Intel Arc 750

C:\Users\rtm\Documents>gputopia-worker-opencl-win-64.exe --ln_url [email protected] --force_layers 33 --test_model TheBloke/CodeLlama-7B-Instruct-GGUF:Q4_K_M --debug
2023-10-09 14:20:42,173 - ai_worker.main - DEBUG - no nvidia: NVML Shared Library Not Found
{'auth_key': '',
'cl_driver_version': 'OpenCL 3.0 ',
'cl_gpus': [{'clock': 2400,
'clock_unit': 'mhz',
'memory': 8319.0,
'name': 'Intel(R) Arc(TM) A750 Graphics',
'uuid': None},
{'clock': 2400,
'clock_unit': 'mhz',
'memory': 8319.0,
'name': 'Intel(R) Arc(TM) A750 Graphics',
'uuid': None},
{'clock': 2400,
'clock_unit': 'mhz',
'memory': 8319.0,
'name': 'Intel(R) Arc(TM) A750 Graphics',
'uuid': None},
{'clock': 2400,
'clock_unit': 'mhz',
'memory': 8319.0,
'name': 'Intel(R) Arc(TM) A750 Graphics',
'uuid': None},
{'clock': 2400,
'clock_unit': 'mhz',
'memory': 8319.0,
'name': 'Intel(R) Arc(TM) A750 Graphics',
'uuid': None}],
'cpu_count': 16,
'disk_space': 409123,
'ln_address': '[email protected]',
'ln_url': '[email protected]',
'nv_driver_version': None,
'nv_gpu_count': None,
'nv_gpus': [],
'vram': 4239507456,
'web_gpus': [],
'worker_id': '',
'worker_version': '0.1.9'}
2023-10-09 14:20:42,610 - ai_worker.main - DEBUG - loading model: TheBloke/CodeLlama-7B-Instruct-GGUF:Q4_K_M
ggml_opencl: selecting platform: 'Intel(R) OpenCL Graphics'
ggml_opencl: selecting device: 'Intel(R) Arc(TM) A750 Graphics'
ggml_opencl: device FP16 support: true
llama_model_loader: loaded meta data with 20 key-value pairs and 291 tensors from C:\Users\rtm.cache\huggingface\hub\models--TheBloke--CodeLlama-7B-Instruct-GGUF\snapshots\2f064ee0c6ae3f025ec4e392c6ba5dd049c77969\codellama-7b-instruct.Q4_K_M.gguf (version GGUF V2 (latest))
llama_model_loader: - tensor 0: token_embd.weight q4_K [ 4096, 32016, 1, 1 ]
llama_model_loader: - tensor 1: blk.0.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 2: blk.0.ffn_down.weight q6_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 3: blk.0.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 4: blk.0.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 5: blk.0.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 6: blk.0.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 7: blk.0.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 8: blk.0.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 9: blk.0.attn_v.weight q6_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 10: blk.1.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 11: blk.1.ffn_down.weight q6_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 12: blk.1.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 13: blk.1.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 14: blk.1.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 15: blk.1.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 16: blk.1.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 17: blk.1.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 18: blk.1.attn_v.weight q6_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 19: blk.10.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 20: blk.10.ffn_down.weight q6_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 21: blk.10.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 22: blk.10.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 23: blk.10.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 24: blk.10.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 25: blk.10.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 26: blk.10.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 27: blk.10.attn_v.weight q6_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 28: blk.11.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 29: blk.11.ffn_down.weight q6_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 30: blk.11.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 31: blk.11.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 32: blk.11.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 33: blk.11.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 34: blk.11.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 35: blk.11.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 36: blk.11.attn_v.weight q6_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 37: blk.12.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 38: blk.12.ffn_down.weight q4_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 39: blk.12.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 40: blk.12.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 41: blk.12.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 42: blk.12.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 43: blk.12.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 44: blk.12.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 45: blk.12.attn_v.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 46: blk.13.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 47: blk.13.ffn_down.weight q4_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 48: blk.13.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 49: blk.13.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 50: blk.13.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 51: blk.13.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 52: blk.13.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 53: blk.13.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 54: blk.13.attn_v.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 55: blk.14.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 56: blk.14.ffn_down.weight q6_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 57: blk.14.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 58: blk.14.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 59: blk.14.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 60: blk.14.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 61: blk.14.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 62: blk.14.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 63: blk.14.attn_v.weight q6_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 64: blk.15.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 65: blk.15.ffn_down.weight q4_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 66: blk.15.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 67: blk.15.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 68: blk.15.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 69: blk.15.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 70: blk.15.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 71: blk.15.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 72: blk.15.attn_v.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 73: blk.16.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 74: blk.16.ffn_down.weight q4_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 75: blk.16.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 76: blk.16.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 77: blk.16.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 78: blk.16.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 79: blk.16.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 80: blk.16.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 81: blk.16.attn_v.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 82: blk.17.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 83: blk.17.ffn_down.weight q6_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 84: blk.17.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 85: blk.17.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 86: blk.17.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 87: blk.17.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 88: blk.17.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 89: blk.17.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 90: blk.17.attn_v.weight q6_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 91: blk.18.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 92: blk.18.ffn_down.weight q4_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 93: blk.18.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 94: blk.18.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 95: blk.18.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 96: blk.18.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 97: blk.18.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 98: blk.18.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 99: blk.18.attn_v.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 100: blk.19.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 101: blk.19.ffn_down.weight q4_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 102: blk.19.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 103: blk.19.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 104: blk.19.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 105: blk.19.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 106: blk.19.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 107: blk.19.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 108: blk.19.attn_v.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 109: blk.2.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 110: blk.2.ffn_down.weight q6_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 111: blk.2.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 112: blk.2.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 113: blk.2.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 114: blk.2.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 115: blk.2.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 116: blk.2.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 117: blk.2.attn_v.weight q6_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 118: blk.20.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 119: blk.20.ffn_down.weight q4_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 120: blk.20.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 121: blk.20.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 122: blk.20.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 123: blk.20.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 124: blk.20.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 125: blk.20.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 126: blk.20.attn_v.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 127: blk.21.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 128: blk.21.ffn_down.weight q4_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 129: blk.21.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 130: blk.21.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 131: blk.21.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 132: blk.21.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 133: blk.21.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 134: blk.21.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 135: blk.21.attn_v.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 136: blk.22.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 137: blk.22.ffn_down.weight q6_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 138: blk.22.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 139: blk.22.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 140: blk.22.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 141: blk.22.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 142: blk.22.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 143: blk.22.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 144: blk.22.attn_v.weight q6_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 145: blk.23.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 146: blk.23.ffn_down.weight q4_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 147: blk.23.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 148: blk.23.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 149: blk.23.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 150: blk.23.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 151: blk.23.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 152: blk.23.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 153: blk.23.attn_v.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 154: blk.3.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 155: blk.3.ffn_down.weight q4_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 156: blk.3.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 157: blk.3.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 158: blk.3.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 159: blk.3.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 160: blk.3.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 161: blk.3.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 162: blk.3.attn_v.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 163: blk.4.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 164: blk.4.ffn_down.weight q6_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 165: blk.4.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 166: blk.4.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 167: blk.4.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 168: blk.4.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 169: blk.4.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 170: blk.4.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 171: blk.4.attn_v.weight q6_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 172: blk.5.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 173: blk.5.ffn_down.weight q4_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 174: blk.5.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 175: blk.5.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 176: blk.5.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 177: blk.5.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 178: blk.5.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 179: blk.5.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 180: blk.5.attn_v.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 181: blk.6.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 182: blk.6.ffn_down.weight q4_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 183: blk.6.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 184: blk.6.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 185: blk.6.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 186: blk.6.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 187: blk.6.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 188: blk.6.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 189: blk.6.attn_v.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 190: blk.7.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 191: blk.7.ffn_down.weight q6_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 192: blk.7.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 193: blk.7.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 194: blk.7.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 195: blk.7.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 196: blk.7.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 197: blk.7.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 198: blk.7.attn_v.weight q6_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 199: blk.8.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 200: blk.8.ffn_down.weight q4_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 201: blk.8.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 202: blk.8.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 203: blk.8.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 204: blk.8.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 205: blk.8.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 206: blk.8.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 207: blk.8.attn_v.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 208: blk.9.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 209: blk.9.ffn_down.weight q4_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 210: blk.9.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 211: blk.9.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 212: blk.9.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 213: blk.9.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 214: blk.9.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 215: blk.9.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 216: blk.9.attn_v.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 217: output.weight q6_K [ 4096, 32016, 1, 1 ]
llama_model_loader: - tensor 218: blk.24.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 219: blk.24.ffn_down.weight q6_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 220: blk.24.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 221: blk.24.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 222: blk.24.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 223: blk.24.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 224: blk.24.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 225: blk.24.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 226: blk.24.attn_v.weight q6_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 227: blk.25.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 228: blk.25.ffn_down.weight q4_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 229: blk.25.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 230: blk.25.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 231: blk.25.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 232: blk.25.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 233: blk.25.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 234: blk.25.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 235: blk.25.attn_v.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 236: blk.26.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 237: blk.26.ffn_down.weight q4_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 238: blk.26.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 239: blk.26.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 240: blk.26.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 241: blk.26.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 242: blk.26.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 243: blk.26.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 244: blk.26.attn_v.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 245: blk.27.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 246: blk.27.ffn_down.weight q6_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 247: blk.27.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 248: blk.27.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 249: blk.27.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 250: blk.27.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 251: blk.27.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 252: blk.27.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 253: blk.27.attn_v.weight q6_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 254: blk.28.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 255: blk.28.ffn_down.weight q6_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 256: blk.28.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 257: blk.28.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 258: blk.28.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 259: blk.28.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 260: blk.28.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 261: blk.28.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 262: blk.28.attn_v.weight q6_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 263: blk.29.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 264: blk.29.ffn_down.weight q6_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 265: blk.29.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 266: blk.29.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 267: blk.29.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 268: blk.29.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 269: blk.29.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 270: blk.29.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 271: blk.29.attn_v.weight q6_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 272: blk.30.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 273: blk.30.ffn_down.weight q6_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 274: blk.30.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 275: blk.30.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 276: blk.30.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 277: blk.30.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 278: blk.30.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 279: blk.30.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 280: blk.30.attn_v.weight q6_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 281: blk.31.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 282: blk.31.ffn_down.weight q6_K [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 283: blk.31.ffn_gate.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 284: blk.31.ffn_up.weight q4_K [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 285: blk.31.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 286: blk.31.attn_k.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 287: blk.31.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 288: blk.31.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 289: blk.31.attn_v.weight q6_K [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 290: output_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - kv 0: general.architecture str
llama_model_loader: - kv 1: general.name str
llama_model_loader: - kv 2: llama.context_length u32
llama_model_loader: - kv 3: llama.embedding_length u32
llama_model_loader: - kv 4: llama.block_count u32
llama_model_loader: - kv 5: llama.feed_forward_length u32
llama_model_loader: - kv 6: llama.rope.dimension_count u32
llama_model_loader: - kv 7: llama.attention.head_count u32
llama_model_loader: - kv 8: llama.attention.head_count_kv u32
llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32
llama_model_loader: - kv 10: llama.rope.freq_base f32
llama_model_loader: - kv 11: general.file_type u32
llama_model_loader: - kv 12: tokenizer.ggml.model str
llama_model_loader: - kv 13: tokenizer.ggml.tokens arr
llama_model_loader: - kv 14: tokenizer.ggml.scores arr
llama_model_loader: - kv 15: tokenizer.ggml.token_type arr
llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32
llama_model_loader: - kv 17: tokenizer.ggml.eos_token_id u32
llama_model_loader: - kv 18: tokenizer.ggml.unknown_token_id u32
llama_model_loader: - kv 19: general.quantization_version u32
llama_model_loader: - type f32: 65 tensors
llama_model_loader: - type q4_K: 193 tensors
llama_model_loader: - type q6_K: 33 tensors
llm_load_print_meta: format = GGUF V2 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 32016
llm_load_print_meta: n_merges = 0
llm_load_print_meta: n_ctx_train = 16384
llm_load_print_meta: n_ctx = 2048
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 32
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_gqa = 1
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: n_ff = 11008
llm_load_print_meta: freq_base = 10000.0
llm_load_print_meta: freq_scale = 1
llm_load_print_meta: model type = 7B
llm_load_print_meta: model ftype = mostly Q4_K - Medium
llm_load_print_meta: model params = 6.74 B
llm_load_print_meta: model size = 3.80 GiB (4.84 BPW)
llm_load_print_meta: general.name = codellama_codellama-7b-instruct-hf
llm_load_print_meta: BOS token = 1 ''
llm_load_print_meta: EOS token = 2 ''
llm_load_print_meta: UNK token = 0 ''
llm_load_print_meta: LF token = 13 '<0x0A>'
llm_load_tensors: ggml ctx size = 0.09 MB
llm_load_tensors: using OpenCL for GPU acceleration
llm_load_tensors: mem required = 173.05 MB (+ 1024.00 MB per state)
llm_load_tensors: offloading 32 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 33/33 layers to GPU
llm_load_tensors: VRAM used: 3719 MB
..............................................................................ggml_opencl: ggml_cl_h2d_tensor_2d(queue, dst, ine0ne1, tensor, i3, i2, NULL) error -5 at C:\Users\erik\AppData\Local\Temp\tmpz3x6681q\llama_cpp_python-0.2.7\vendor\llama.cpp\ggml-opencl.cpp:1857

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Unhandled exception on req_str

Running the latest workbee 0.2.2 (latest from today)

Got the following exception:

2023-10-27 22:19:13,397 - httpx - INFO - HTTP Request: POST http://test/v1/chat/completions "HTTP/1.1 200 OK"
2023-10-27 22:19:13,398 - ai_worker.main - INFO - done TheBloke/vicuna-7B-v1.5-GGUF:Q4_K_M (5.875 secs)
Traceback (most recent call last):
  File "ai_worker\main.py", line 357, in run_one
  File "ai_worker\main.py", line 338, in ws_recv
  File "websockets\legacy\protocol.py", line 568, in recv
  File "websockets\legacy\protocol.py", line 944, in ensure_open
websockets.exceptions.ConnectionClosedError: received 1012 (service restart); then sent 1012 (service restart)

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "ai_worker\__main__.py", line 4, in <module>
  File "ai_worker\main.py", line 504, in main
  File "asyncio\runners.py", line 190, in run
  File "asyncio\runners.py", line 118, in run
  File "asyncio\base_events.py", line 653, in run_until_complete
  File "ai_worker\main.py", line 201, in run
  File "ai_worker\main.py", line 347, in run_ws
  File "ai_worker\main.py", line 382, in run_one
UnboundLocalError: cannot access local variable 'req_str' where it is not associated with a value
[14004] Failed to execute script '__main__' due to unhandled exception!

Hope this helps.... After a restart everything works fine again... seems to happen when possibly the websocket connection is lost.

I had when i wrote a bot for the deribit exchange also always issues with asyncio handling it. After very long struggles i switched to the python library lomond since then i never had a issue again.... (https://github.com/wildfoundry/dataplicity-lomond) (automatic reconnect, handling of different tcp / ip protocol errors, disconnect handling)

Better reconnect mechanism

If a connection gets lost in the middle of a job, it would be cool if the worker reconnected and sent the completed results. The spider could then abort any retry attempts and use them,

Connection error

Hey, I installed workerbee on my linux machine, I tried running ./gputopia-worker but I get this connection error

2023-09-19 22:24:12,013 - websockets.client - INFO - ! connect failed; reconnecting in 0.4 seconds
Traceback (most recent call last):                                                                                                                                                                                                             File "websockets/legacy/client.py", line 602, in __aiter__
File "websockets/legacy/client.py", line 637, in __aenter__
File "websockets/legacy/client.py", line 655, in __await_impl_timeout__
File "websockets/legacy/client.py", line 659, in __await_impl__
File "asyncio/base_events.py", line 1036, in create_connection
File "asyncio/base_events.py", line 1418, in _ensure_resolved
File "asyncio/base_events.py", line 863, in getaddrinfo
File "concurrent/futures/thread.py", line 58, in run
File "socket.py", line 955, in getaddrinfo
socket.gaierror: [Errno -2] Name or service not known

I can provide further input if needed

--main_gpu does not select specified gpu - Intel Arc GPUs

C:\Users\rtm\Documents>gputopia-worker-opencl-win-64.exe --ln_url [email protected] --force_layers 33 --test_model TheBloke/CodeLlama-7B-Instruct-GGUF:Q4_K_M --debug --main_gpu 0 --tensor_split 0,1,0,1,0,1,0,1,0,1
2023-10-07 10:52:12,482 - ai_worker.main - DEBUG - no nvidia: NVML Shared Library Not Found
{'auth_key': '',
'cl_driver_version': 'OpenCL 3.0 ',
'cl_gpus': [{'clock': 2400,
'clock_unit': 'mhz',
'memory': 8319.0,
'name': 'Intel(R) Arc(TM) A750 Graphics',
'uuid': None},
{'clock': 2400,
'clock_unit': 'mhz',
'memory': 8319.0,
'name': 'Intel(R) Arc(TM) A750 Graphics',
'uuid': None},
{'clock': 2400,
'clock_unit': 'mhz',
'memory': 8319.0,
'name': 'Intel(R) Arc(TM) A750 Graphics',
'uuid': None},
{'clock': 2400,
'clock_unit': 'mhz',
'memory': 8319.0,
'name': 'Intel(R) Arc(TM) A750 Graphics',
'uuid': None},
{'clock': 2400,
'clock_unit': 'mhz',
'memory': 8319.0,
'name': 'Intel(R) Arc(TM) A750 Graphics',
'uuid': None}],
'cpu_count': 16,
'disk_space': 428351,
'ln_address': '[email protected]',
'ln_url': '[email protected]',
'nv_driver_version': None,
'nv_gpu_count': None,
'nv_gpus': [],
'vram': 5996646400,
'web_gpus': [],
'worker_id': '',
'worker_version': '0.1.9'}
2023-10-07 10:52:13,051 - ai_worker.main - DEBUG - loading model: TheBloke/CodeLlama-7B-Instruct-GGUF:Q4_K_M
ggml_opencl: selecting platform: 'Intel(R) OpenCL Graphics'
ggml_opencl: selecting device: 'Intel(R) Arc(TM) A750 Graphics'
ggml_opencl: device FP16 support: true
Traceback (most recent call last):
File "ai_worker_main_.py", line 4, in
File "ai_worker\main.py", line 385, in main
File "asyncio\runners.py", line 190, in run
File "asyncio\runners.py", line 118, in run
File "asyncio\base_events.py", line 653, in run_until_complete
File "ai_worker\main.py", line 144, in run
File "ai_worker\main.py", line 114, in test_model
File "ai_worker\main.py", line 201, in load_model
File "llama_cpp\server\app.py", line 342, in create_app
File "llama_cpp\llama.py", line 312, in init
IndexError: invalid index
[12344] Failed to execute script 'main' due to unhandled exception!

image

--tensor_split not working either

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