Cannot serve `CohereForAI/c4ai-command-r-plus-4bit`

[phisad]

I just wanted to serve the CohereForAI/c4ai-command-r-plus-4bit model, but after I installed bitsandbytes I get this error when running:

    entrypoint: [ "python3.9", "-m", "fastchat.serve.model_worker",
                  "--model-names", "command-r-plus-4bit",
                  "--model-path", "CohereForAI/c4ai-command-r-plus-4bit",
                  "--worker-address", "http://fsc-model-gpu3-1:31001",
                  "--controller-address", "http://fsc-control:21001",
                  "--host", "0.0.0.0",
                  "--port", "31001" ]

Some weights of the model checkpoint at CohereForAI/c4ai-command-r-plus-4bit were not used when initializing CohereForCausalLM: ['model.layers.0.self_attn.k_norm.weight', 'model.layers.0.self_attn.q_norm.weight', 'model.layers.1.self_attn.k_norm.weight', 'model.layers.1.self_attn.q_norm.weight', 'model.layers.10.self_attn.k_norm.weight', 'model.layers.10.self_attn.q_norm.weight', 'model.layers.11.self_attn.k_norm.weight', 'model.layers.11.self_attn.q_norm.weight', 'model.layers.12.self_attn.k_norm.weight', 'model.layers.12.self_attn.q_norm.weight', 'model.layers.13.self_attn.k_norm.weight', 'model.layers.13.self_attn.q_norm.weight', 'model.layers.14.self_attn.k_norm.weight', 'model.layers.14.self_attn.q_norm.weight', 'model.layers.15.self_attn.k_norm.weight', 'model.layers.15.self_attn.q_norm.weight', 'model.layers.16.self_attn.k_norm.weight', 'model.layers.16.self_attn.q_norm.weight', 'model.layers.17.self_attn.k_norm.weight', 'model.layers.17.self_attn.q_norm.weight', 'model.layers.18.self_attn.k_norm.weight', 'model.layers.18.self_attn.q_norm.weight', 'model.layers.19.self_attn.k_norm.weight', 'model.layers.19.self_attn.q_norm.weight', 'model.layers.2.self_attn.k_norm.weight', 'model.layers.2.self_attn.q_norm.weight', 'model.layers.20.self_attn.k_norm.weight', 'model.layers.20.self_attn.q_norm.weight', 'model.layers.21.self_attn.k_norm.weight', 'model.layers.21.self_attn.q_norm.weight', 'model.layers.22.self_attn.k_norm.weight', 'model.layers.22.self_attn.q_norm.weight', 'model.layers.23.self_attn.k_norm.weight', 'model.layers.23.self_attn.q_norm.weight', 'model.layers.24.self_attn.k_norm.weight', 'model.layers.24.self_attn.q_norm.weight', 'model.layers.25.self_attn.k_norm.weight', 'model.layers.25.self_attn.q_norm.weight', 'model.layers.26.self_attn.k_norm.weight', 'model.layers.26.self_attn.q_norm.weight', 'model.layers.27.self_attn.k_norm.weight', 'model.layers.27.self_attn.q_norm.weight', 'model.layers.28.self_attn.k_norm.weight', 'model.layers.28.self_attn.q_norm.weight', 'model.layers.29.self_attn.k_norm.weight', 'model.layers.29.self_attn.q_norm.weight', 'model.layers.3.self_attn.k_norm.weight', 'model.layers.3.self_attn.q_norm.weight', 'model.layers.30.self_attn.k_norm.weight', 'model.layers.30.self_attn.q_norm.weight', 'model.layers.31.self_attn.k_norm.weight', 'model.layers.31.self_attn.q_norm.weight', 'model.layers.32.self_attn.k_norm.weight', 'model.layers.32.self_attn.q_norm.weight', 'model.layers.33.self_attn.k_norm.weight', 'model.layers.33.self_attn.q_norm.weight', 'model.layers.34.self_attn.k_norm.weight', 'model.layers.34.self_attn.q_norm.weight', 'model.layers.35.self_attn.k_norm.weight', 'model.layers.35.self_attn.q_norm.weight', 'model.layers.36.self_attn.k_norm.weight', 'model.layers.36.self_attn.q_norm.weight', 'model.layers.37.self_attn.k_norm.weight', 'model.layers.37.self_attn.q_norm.weight', 'model.layers.38.self_attn.k_norm.weight', 'model.layers.38.self_attn.q_norm.weight', 'model.layers.39.self_attn.k_norm.weight', 'model.layers.39.self_attn.q_norm.weight', 'model.layers.4.self_attn.k_norm.weight', 'model.layers.4.self_attn.q_norm.weight', 'model.layers.40.self_attn.k_norm.weight', 'model.layers.40.self_attn.q_norm.weight', 'model.layers.41.self_attn.k_norm.weight', 'model.layers.41.self_attn.q_norm.weight', 'model.layers.42.self_attn.k_norm.weight', 'model.layers.42.self_attn.q_norm.weight', 'model.layers.43.self_attn.k_norm.weight', 'model.layers.43.self_attn.q_norm.weight', 'model.layers.44.self_attn.k_norm.weight', 'model.layers.44.self_attn.q_norm.weight', 'model.layers.45.self_attn.k_norm.weight', 'model.layers.45.self_attn.q_norm.weight', 'model.layers.46.self_attn.k_norm.weight', 'model.layers.46.self_attn.q_norm.weight', 'model.layers.47.self_attn.k_norm.weight', 'model.layers.47.self_attn.q_norm.weight', 'model.layers.48.self_attn.k_norm.weight', 'model.layers.48.self_attn.q_norm.weight', 'model.layers.49.self_attn.k_norm.weight', 'model.layers.49.self_attn.q_norm.weight', 'model.layers.5.self_attn.k_norm.weight', 'model.layers.5.self_attn.q_norm.weight', 'model.layers.50.self_attn.k_norm.weight', 'model.layers.50.self_attn.q_norm.weight', 'model.layers.51.self_attn.k_norm.weight', 'model.layers.51.self_attn.q_norm.weight', 'model.layers.52.self_attn.k_norm.weight', 'model.layers.52.self_attn.q_norm.weight', 'model.layers.53.self_attn.k_norm.weight', 'model.layers.53.self_attn.q_norm.weight', 'model.layers.54.self_attn.k_norm.weight', 'model.layers.54.self_attn.q_norm.weight', 'model.layers.55.self_attn.k_norm.weight', 'model.layers.55.self_attn.q_norm.weight', 'model.layers.56.self_attn.k_norm.weight', 'model.layers.56.self_attn.q_norm.weight', 'model.layers.57.self_attn.k_norm.weight', 'model.layers.57.self_attn.q_norm.weight', 'model.layers.58.self_attn.k_norm.weight', 'model.layers.58.self_attn.q_norm.weight', 'model.layers.59.self_attn.k_norm.weight', 'model.layers.59.self_attn.q_norm.weight', 'model.layers.6.self_attn.k_norm.weight', 'model.layers.6.self_attn.q_norm.weight', 'model.layers.60.self_attn.k_norm.weight', 'model.layers.60.self_attn.q_norm.weight', 'model.layers.61.self_attn.k_norm.weight', 'model.layers.61.self_attn.q_norm.weight', 'model.layers.62.self_attn.k_norm.weight', 'model.layers.62.self_attn.q_norm.weight', 'model.layers.63.self_attn.k_norm.weight', 'model.layers.63.self_attn.q_norm.weight', 'model.layers.7.self_attn.k_norm.weight', 'model.layers.7.self_attn.q_norm.weight', 'model.layers.8.self_attn.k_norm.weight', 'model.layers.8.self_attn.q_norm.weight', 'model.layers.9.self_attn.k_norm.weight', 'model.layers.9.self_attn.q_norm.weight']
- This IS expected if you are initializing CohereForCausalLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing CohereForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
2024-04-16 11:23:21 | WARNING | accelerate.big_modeling | You shouldn't move a model that is dispatched using accelerate hooks.
2024-04-16 11:23:21 | ERROR | stderr | Traceback (most recent call last):
2024-04-16 11:23:21 | ERROR | stderr |   File "/usr/lib/python3.9/runpy.py", line 197, in _run_module_as_main
2024-04-16 11:23:21 | ERROR | stderr |     return _run_code(code, main_globals, None,
2024-04-16 11:23:21 | ERROR | stderr |   File "/usr/lib/python3.9/runpy.py", line 87, in _run_code
2024-04-16 11:23:21 | ERROR | stderr |     exec(code, run_globals)
2024-04-16 11:23:21 | ERROR | stderr |   File "/opt/FastChat/fastchat/serve/model_worker.py", line 414, in <module>
2024-04-16 11:23:21 | ERROR | stderr |     args, worker = create_model_worker()
2024-04-16 11:23:21 | ERROR | stderr |   File "/opt/FastChat/fastchat/serve/model_worker.py", line 385, in create_model_worker
2024-04-16 11:23:21 | ERROR | stderr |     worker = ModelWorker(
2024-04-16 11:23:21 | ERROR | stderr |   File "/opt/FastChat/fastchat/serve/model_worker.py", line 77, in __init__
2024-04-16 11:23:21 | ERROR | stderr |     self.model, self.tokenizer = load_model(
2024-04-16 11:23:21 | ERROR | stderr |   File "/opt/FastChat/fastchat/model/model_adapter.py", line 376, in load_model
2024-04-16 11:23:21 | ERROR | stderr |     model.to(device)
2024-04-16 11:23:21 | ERROR | stderr |   File "/usr/local/lib/python3.9/dist-packages/accelerate/big_modeling.py", line 456, in wrapper
2024-04-16 11:23:21 | ERROR | stderr |     return fn(*args, **kwargs)
2024-04-16 11:23:21 | ERROR | stderr |   File "/usr/local/lib/python3.9/dist-packages/transformers/modeling_utils.py", line 2554, in to
2024-04-16 11:23:21 | ERROR | stderr |     raise ValueError(
2024-04-16 11:23:21 | ERROR | stderr | ValueError: `.to` is not supported for `4-bit` or `8-bit` bitsandbytes models. Please use the model as it is, since the model has already been set to the correct devices and casted to the correct `dtype`

[valueLzy]

I'm having the same issue, please, is it solved?