i was able to start the application with 0.4.0 but when i try to start with 0.5.0, i am getting following output. Please help.
(gpt) C:\Users\genco\Desktop\docs\private-gpt-main>make run
poetry run python -m private_gpt
17:48:27.791 [INFO ] private_gpt.settings.settings_loader - Starting application with profiles=['default', 'local']
17:48:34.709 [INFO ] private_gpt.components.llm.llm_component - Initializing the LLM in mode=llamacpp
llama_model_loader: loaded meta data with 24 key-value pairs and 291 tensors from C:\Users\genco\Desktop\docs\private-gpt-main\models\mistral-7b-instruct-v0.2.Q8_0.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = llama
llama_model_loader: - kv 1: general.name str = mistralai_mistral-7b-instruct-v0.2
llama_model_loader: - kv 2: llama.context_length u32 = 32768
llama_model_loader: - kv 3: llama.embedding_length u32 = 4096
llama_model_loader: - kv 4: llama.block_count u32 = 32
llama_model_loader: - kv 5: llama.feed_forward_length u32 = 14336
llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 7: llama.attention.head_count u32 = 32
llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 10: llama.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 11: general.file_type u32 = 7
llama_model_loader: - kv 12: tokenizer.ggml.model str = llama
llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,32000] = ["", "", "", "<0x00>", "<...
llama_model_loader: - kv 14: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32 = 1
llama_model_loader: - kv 17: tokenizer.ggml.eos_token_id u32 = 2
llama_model_loader: - kv 18: tokenizer.ggml.unknown_token_id u32 = 0
llama_model_loader: - kv 19: tokenizer.ggml.padding_token_id u32 = 0
llama_model_loader: - kv 20: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 21: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 22: tokenizer.chat_template str = {{ bos_token }}{% for message in mess...
llama_model_loader: - kv 23: general.quantization_version u32 = 2
llama_model_loader: - type f32: 65 tensors
llama_model_loader: - type q8_0: 226 tensors
llm_load_vocab: special tokens definition check successful ( 259/32000 ).
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 32000
llm_load_print_meta: n_merges = 0
llm_load_print_meta: n_ctx_train = 32768
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 4
llm_load_print_meta: n_embd_k_gqa = 1024
llm_load_print_meta: n_embd_v_gqa = 1024
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: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 14336
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: causal attn = 1
llm_load_print_meta: pooling type = 0
llm_load_print_meta: rope type = 0
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 1000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx = 32768
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: ssm_d_conv = 0
llm_load_print_meta: ssm_d_inner = 0
llm_load_print_meta: ssm_d_state = 0
llm_load_print_meta: ssm_dt_rank = 0
llm_load_print_meta: model type = 7B
llm_load_print_meta: model ftype = Q8_0
llm_load_print_meta: model params = 7.24 B
llm_load_print_meta: model size = 7.17 GiB (8.50 BPW)
llm_load_print_meta: general.name = mistralai_mistral-7b-instruct-v0.2
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: PAD token = 0 ''
llm_load_print_meta: LF token = 13 '<0x0A>'
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 4050 Laptop GPU, compute capability 8.9, VMM: yes
llm_load_tensors: ggml ctx size = 0.22 MiB
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: CPU buffer size = 132.81 MiB
llm_load_tensors: CUDA0 buffer size = 7205.83 MiB
...................................................................................................
llama_new_context_with_model: n_ctx = 32000
llama_new_context_with_model: n_batch = 512
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: freq_base = 1000000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CUDA0 KV buffer size = 4000.00 MiB
llama_new_context_with_model: KV self size = 4000.00 MiB, K (f16): 2000.00 MiB, V (f16): 2000.00 MiB
llama_new_context_with_model: CUDA_Host output buffer size = 0.12 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 2094.50 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 70.51 MiB
llama_new_context_with_model: graph nodes = 1030
llama_new_context_with_model: graph splits = 2
AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 0 | VSX = 0 | MATMUL_INT8 = 0 |
Model metadata: {'general.name': 'mistralai_mistral-7b-instruct-v0.2', 'general.architecture': 'llama', 'llama.context_length': '32768', 'llama.rope.dimension_count': '128', 'llama.embedding_length': '4096', 'llama.block_count': '32', 'llama.feed_forward_length': '14336', 'llama.attention.head_count': '32', 'tokenizer.ggml.eos_token_id': '2', 'general.file_type': '7', 'llama.attention.head_count_kv': '8', 'llama.attention.layer_norm_rms_epsilon': '0.000010', 'llama.rope.freq_base': '1000000.000000', 'tokenizer.ggml.model': 'llama', 'general.quantization_version': '2', 'tokenizer.ggml.bos_token_id': '1', 'tokenizer.ggml.unknown_token_id': '0', 'tokenizer.ggml.padding_token_id': '0', 'tokenizer.ggml.add_bos_token': 'true', 'tokenizer.ggml.add_eos_token': 'false', 'tokenizer.chat_template': "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}"}
Guessed chat format: mistral-instruct
17:48:39.584 [INFO ] private_gpt.components.embedding.embedding_component - Initializing the embedding model in mode=huggingface
Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████| 3/3 [00:14<00:00, 4.98s/it]
Traceback (most recent call last):
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init_.py", line 798, in get
return self._context[key]
~~~~~~~~~~~~~^^^^^
KeyError: <class 'private_gpt.ui.ui.PrivateGptUi'>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init_.py", line 798, in get
return self._context[key]
~~~~~~~~~~~~~^^^^^
KeyError: <class 'private_gpt.server.ingest.ingest_service.IngestService'>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init_.py", line 798, in get
return self._context[key]
~~~~~~~~~~~~~^^^^^
KeyError: <class 'private_gpt.components.embedding.embedding_component.EmbeddingComponent'>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "", line 198, in run_module_as_main
File "", line 88, in run_code
File "C:\Users\genco\Desktop\docs\private-gpt-main\private_gpt_main.py", line 5, in
from private_gpt.main import app
File "C:\Users\genco\Desktop\docs\private-gpt-main\private_gpt\main.py", line 6, in
app = create_app(global_injector)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\genco\Desktop\docs\private-gpt-main\private_gpt\launcher.py", line 63, in create_app
ui = root_injector.get(PrivateGptUi)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init.py", line 91, in wrapper
return function(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init_.py", line 974, in get
provider_instance = scope_instance.get(interface, binding.provider)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init_.py", line 91, in wrapper
return function(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init_.py", line 800, in get
instance = self.get_instance(key, provider, self.injector)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init.py", line 811, in get_instance
return provider.get(injector)
^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init.py", line 264, in get
return injector.create_object(self.cls)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init.py", line 998, in create_object
self.call_with_injection(init, self_=instance, kwargs=additional_kwargs)
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init_.py", line 1031, in call_with_injection
dependencies = self.args_to_inject(
^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init_.py", line 91, in wrapper
return function(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init_.py", line 1079, in args_to_inject
instance: Any = self.get(interface)
^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init_.py", line 91, in wrapper
return function(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init_.py", line 974, in get
provider_instance = scope_instance.get(interface, binding.provider)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init_.py", line 91, in wrapper
return function(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init_.py", line 800, in get
instance = self.get_instance(key, provider, self.injector)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init.py", line 811, in get_instance
return provider.get(injector)
^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init.py", line 264, in get
return injector.create_object(self.cls)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init.py", line 998, in create_object
self.call_with_injection(init, self_=instance, kwargs=additional_kwargs)
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init_.py", line 1031, in call_with_injection
dependencies = self.args_to_inject(
^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init_.py", line 91, in wrapper
return function(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init_.py", line 1079, in args_to_inject
instance: Any = self.get(interface)
^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init_.py", line 91, in wrapper
return function(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init_.py", line 974, in get
provider_instance = scope_instance.get(interface, binding.provider)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init_.py", line 91, in wrapper
return function(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init_.py", line 800, in get
instance = self.get_instance(key, provider, self.injector)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init.py", line 811, in get_instance
return provider.get(injector)
^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init.py", line 264, in get
return injector.create_object(self.cls)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init.py", line 998, in create_object
self.call_with_injection(init, self_=instance, kwargs=additional_kwargs)
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\injector_init_.py", line 1040, in call_with_injection
return callable(*full_args, **dependencies)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\genco\Desktop\docs\private-gpt-main\private_gpt\components\embedding\embedding_component.py", line 31, in init
self.embedding_model = HuggingFaceEmbedding(
^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\llama_index\embeddings\huggingface\base.py", line 87, in init
self._model = model.to(self._device)
^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\transformers\modeling_utils.py", line 2556, in to
return super().to(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\torch\nn\modules\module.py", line 1152, in to
return self._apply(convert)
^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\torch\nn\modules\module.py", line 802, in _apply
module._apply(fn)
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\torch\nn\modules\module.py", line 802, in _apply
module._apply(fn)
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\torch\nn\modules\module.py", line 802, in _apply
module._apply(fn)
[Previous line repeated 1 more time]
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\torch\nn\modules\module.py", line 825, in _apply
param_applied = fn(param)
^^^^^^^^^
File "C:\ProgramData\anaconda3\envs\gpt\Lib\site-packages\torch\nn\modules\module.py", line 1150, in convert
return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 64.00 MiB. GPU 0 has a total capacity of 6.00 GiB of which 0 bytes is free. Of the allocated memory 23.36 GiB is allocated by PyTorch, and 1.14 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
make: *** [Makefile:36: run] Error 1
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