[BUG] GUI not working

[ashunaveed]

The GUI is not working.

(gpt1) ashu@MSI:/mnt/c/Users/genco/Documents/gpt$ make run
poetry run python -m private_gpt
11:47:15.734 [INFO ] private_gpt.settings.settings_loader - Starting application with profiles=['default', 'local']
11:47:21.826 [INFO ] private_gpt.components.llm.llm_component - Initializing the LLM in mode=llamacpp
11:47:21.842 [WARNING ] py.warnings - /home/ashu/.cache/pypoetry/virtualenvs/private-gpt-I_MJhhDm-py3.11/lib/python3.11/site-packages/pydantic/_internal/fields.py:132: UserWarning: Field "model_url" in LlamaCPP has conflict with protected namespace "model".

You may be able to resolve this warning by setting model_config['protected_namespaces'] = ().
warnings.warn(

11:47:21.842 [WARNING ] py.warnings - /home/ashu/.cache/pypoetry/virtualenvs/private-gpt-I_MJhhDm-py3.11/lib/python3.11/site-packages/pydantic/_internal/fields.py:132: UserWarning: Field "model_path" in LlamaCPP has conflict with protected namespace "model".

You may be able to resolve this warning by setting model_config['protected_namespaces'] = ().
warnings.warn(

11:47:21.843 [WARNING ] py.warnings - /home/ashu/.cache/pypoetry/virtualenvs/private-gpt-I_MJhhDm-py3.11/lib/python3.11/site-packages/pydantic/_internal/fields.py:132: UserWarning: Field "model_kwargs" in LlamaCPP has conflict with protected namespace "model".

You may be able to resolve this warning by setting model_config['protected_namespaces'] = ().
warnings.warn(

llama_model_loader: loaded meta data with 33 key-value pairs and 292 tensors from /mnt/c/Users/genco/Documents/gpt/models/Meta-Llama-3.1-8B-Instruct-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.type str = model
llama_model_loader: - kv 2: general.name str = Meta Llama 3.1 8B Instruct
llama_model_loader: - kv 3: general.finetune str = Instruct
llama_model_loader: - kv 4: general.basename str = Meta-Llama-3.1
llama_model_loader: - kv 5: general.size_label str = 8B
llama_model_loader: - kv 6: general.license str = llama3.1
llama_model_loader: - kv 7: general.tags arr[str,6] = ["facebook", "meta", "pytorch", "llam...
llama_model_loader: - kv 8: general.languages arr[str,8] = ["en", "de", "fr", "it", "pt", "hi", ...
llama_model_loader: - kv 9: llama.block_count u32 = 32
llama_model_loader: - kv 10: llama.context_length u32 = 131072
llama_model_loader: - kv 11: llama.embedding_length u32 = 4096
llama_model_loader: - kv 12: llama.feed_forward_length u32 = 14336
llama_model_loader: - kv 13: llama.attention.head_count u32 = 32
llama_model_loader: - kv 14: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 15: llama.rope.freq_base f32 = 500000.000000
llama_model_loader: - kv 16: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 17: general.file_type u32 = 7
llama_model_loader: - kv 18: llama.vocab_size u32 = 128256
llama_model_loader: - kv 19: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 20: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 21: tokenizer.ggml.pre str = llama-bpe
llama_model_loader: - kv 22: tokenizer.ggml.tokens arr[str,128256] = ["!", """, "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 23: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 24: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
llama_model_loader: - kv 25: tokenizer.ggml.bos_token_id u32 = 128000
llama_model_loader: - kv 26: tokenizer.ggml.eos_token_id u32 = 128009
llama_model_loader: - kv 27: tokenizer.chat_template str = {{- bos_token }}\n{%- if custom_tools ...
llama_model_loader: - kv 28: general.quantization_version u32 = 2
llama_model_loader: - kv 29: quantize.imatrix.file str = /models_out/Meta-Llama-3.1-8B-Instruc...
llama_model_loader: - kv 30: quantize.imatrix.dataset str = /training_dir/calibration_datav3.txt
llama_model_loader: - kv 31: quantize.imatrix.entries_count i32 = 224
llama_model_loader: - kv 32: quantize.imatrix.chunks_count i32 = 125
llama_model_loader: - type f32: 66 tensors
llama_model_loader: - type q8_0: 226 tensors
llm_load_vocab: special tokens cache size = 256
llm_load_vocab: token to piece cache size = 0.7999 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 128256
llm_load_print_meta: n_merges = 280147
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 131072
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_swa = 0
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 = 500000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 131072
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: ssm_dt_b_c_rms = 0
llm_load_print_meta: model type = 8B
llm_load_print_meta: model ftype = Q8_0
llm_load_print_meta: model params = 8.03 B
llm_load_print_meta: model size = 7.95 GiB (8.50 BPW)
llm_load_print_meta: general.name = Meta Llama 3.1 8B Instruct
llm_load_print_meta: BOS token = 128000 '<|begin_of_text|>'
llm_load_print_meta: EOS token = 128009 '<|eot_id|>'
llm_load_print_meta: LF token = 128 'Ä'
llm_load_print_meta: EOT token = 128009 '<|eot_id|>'
llm_load_print_meta: max token length = 256
llm_load_tensors: ggml ctx size = 0.14 MiB
llm_load_tensors: CPU buffer size = 8137.64 MiB
.........................................................................................
llama_new_context_with_model: n_ctx = 3904
llama_new_context_with_model: n_batch = 512
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base = 500000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CPU KV buffer size = 488.00 MiB
llama_new_context_with_model: KV self size = 488.00 MiB, K (f16): 244.00 MiB, V (f16): 244.00 MiB
llama_new_context_with_model: CPU output buffer size = 0.49 MiB
llama_new_context_with_model: CPU compute buffer size = 283.63 MiB
llama_new_context_with_model: graph nodes = 1030
llama_new_context_with_model: graph splits = 1
AVX = 1 | AVX_VNNI = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 |
Model metadata: {'quantize.imatrix.entries_count': '224', 'quantize.imatrix.dataset': '/training_dir/calibration_datav3.txt', 'quantize.imatrix.chunks_count': '125', 'quantize.imatrix.file': '/models_out/Meta-Llama-3.1-8B-Instruct-GGUF/Meta-Llama-3.1-8B-Instruct.imatrix', 'tokenizer.chat_template': '{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- set date_string = "26 Jul 2024" %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = "" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}\n{%- if builtin_tools is defined or tools is not none %}\n {{- "Environment: ipython\n" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n {{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}\n{%- endif %}\n{{- "Cutting Knowledge Date: December 2023\n" }}\n{{- "Today Date: " + date_string + "\n\n" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}\n {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}\n {{- "Do not use variables.\n\n" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- "\n\n" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- "<|eot_id|>" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}\n {{- "Given the following functions, please respond with a JSON for a function call " }}\n {{- "with its proper arguments that best answers the given prompt.\n\n" }}\n {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}\n {{- "Do not use variables.\n\n" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- "\n\n" }}\n {%- endfor %}\n {{- first_user_message + "<|eot_id|>"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception("This model only supports single tool-calls at once!") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}\n {{- "<|python_tag|>" + tool_call.name + ".call(" }}\n {%- for arg_name, arg_val in tool_call.arguments | items %}\n {{- arg_name + '="' + arg_val + '"' }}\n {%- if not loop.last %}\n {{- ", " }}\n {%- endif %}\n {%- endfor %}\n {{- ")" }}\n {%- else %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}\n {{- '{"name": "' + tool_call.name + '", ' }}\n {{- '"parameters": ' }}\n {{- tool_call.arguments | tojson }}\n {{- "}" }}\n {%- endif %}\n {%- if builtin_tools is defined %}\n {#- This means we're in ipython mode #}\n {{- "<|eom_id|>" }}\n {%- else %}\n {{- "<|eot_id|>" }}\n {%- endif %}\n {%- elif message.role == "tool" or message.role == "ipython" %}\n {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- "<|eot_id|>" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}\n{%- endif %}\n', 'tokenizer.ggml.eos_token_id': '128009', 'general.quantization_version': '2', 'tokenizer.ggml.model': 'gpt2', 'llama.rope.dimension_count': '128', 'llama.vocab_size': '128256', 'general.file_type': '7', 'llama.attention.layer_norm_rms_epsilon': '0.000010', 'llama.rope.freq_base': '500000.000000', 'general.architecture': 'llama', 'general.basename': 'Meta-Llama-3.1', 'tokenizer.ggml.bos_token_id': '128000', 'llama.attention.head_count': '32', 'tokenizer.ggml.pre': 'llama-bpe', 'llama.context_length': '131072', 'general.name': 'Meta Llama 3.1 8B Instruct', 'general.finetune': 'Instruct', 'general.type': 'model', 'general.size_label': '8B', 'general.license': 'llama3.1', 'llama.feed_forward_length': '14336', 'llama.embedding_length': '4096', 'llama.block_count': '32', 'llama.attention.head_count_kv': '8'}
Available chat formats from metadata: chat_template.default
Using gguf chat template: {{- bos_token }}
{%- if custom_tools is defined %}
{%- set tools = custom_tools %}
{%- endif %}
{%- if not tools_in_user_message is defined %}
{%- set tools_in_user_message = true %}
{%- endif %}
{%- if not date_string is defined %}
{%- set date_string = "26 Jul 2024" %}
{%- endif %}

{#- This block extracts the system message, so we can slot it into the right place. #}
{%- if messages[0]['role'] == 'system' %}
{%- set system_message = messages[0]['content']|trim %}
{%- set messages = messages[1:] %}
{%- else %}
{%- set system_message = "" %}
{%- endif %}

{#- System message + builtin tools #}
{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
{%- if builtin_tools is defined or tools is not none %}
{{- "Environment: ipython\n" }}
{%- endif %}
{%- if builtin_tools is defined %}
{{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
{%- endif %}
{{- "Cutting Knowledge Date: December 2023\n" }}
{{- "Today Date: " + date_string + "\n\n" }}
{%- if tools is not none and not tools_in_user_message %}
{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
{{- "Do not use variables.\n\n" }}
{%- for t in tools %}
{{- t | tojson(indent=4) }}
{{- "\n\n" }}
{%- endfor %}
{%- endif %}
{{- system_message }}
{{- "<|eot_id|>" }}

{#- Custom tools are passed in a user message with some extra guidance #}
{%- if tools_in_user_message and not tools is none %}
{#- Extract the first user message so we can plug it in here #}
{%- if messages | length != 0 %}
{%- set first_user_message = messages[0]['content']|trim %}
{%- set messages = messages[1:] %}
{%- else %}
{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
{%- endif %}
{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
{{- "Given the following functions, please respond with a JSON for a function call " }}
{{- "with its proper arguments that best answers the given prompt.\n\n" }}
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
{{- "Do not use variables.\n\n" }}
{%- for t in tools %}
{{- t | tojson(indent=4) }}
{{- "\n\n" }}
{%- endfor %}
{{- first_user_message + "<|eot_id|>"}}
{%- endif %}

{%- for message in messages %}
{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
{%- elif 'tool_calls' in message %}
{%- if not message.tool_calls|length == 1 %}
{{- raise_exception("This model only supports single tool-calls at once!") }}
{%- endif %}
{%- set tool_call = message.tool_calls[0].function %}
{%- if builtin_tools is defined and tool_call.name in builtin_tools %}
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
{{- "<|python_tag|>" + tool_call.name + ".call(" }}
{%- for arg_name, arg_val in tool_call.arguments | items %}
{{- arg_name + '="' + arg_val + '"' }}
{%- if not loop.last %}
{{- ", " }}
{%- endif %}
{%- endfor %}
{{- ")" }}
{%- else %}
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
{{- '{"name": "' + tool_call.name + '", ' }}
{{- '"parameters": ' }}
{{- tool_call.arguments | tojson }}
{{- "}" }}
{%- endif %}
{%- if builtin_tools is defined %}
{#- This means we're in ipython mode #}
{{- "<|eom_id|>" }}
{%- else %}
{{- "<|eot_id|>" }}
{%- endif %}
{%- elif message.role == "tool" or message.role == "ipython" %}
{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
{%- if message.content is mapping or message.content is iterable %}
{{- message.content | tojson }}
{%- else %}
{{- message.content }}
{%- endif %}
{{- "<|eot_id|>" }}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
{%- endif %}

Using chat eos_token: <|eot_id|>
Using chat bos_token: <|begin_of_text|>
11:51:08.848 [INFO ] private_gpt.components.embedding.embedding_component - Initializing the embedding model in mode=huggingface
11:51:09.426 [INFO ] sentence_transformers.SentenceTransformer - Load pretrained SentenceTransformer: dunzhang/stella_en_1.5B_v5
11:53:45.124 [INFO ] sentence_transformers.SentenceTransformer - 2 prompts are loaded, with the keys: ['query', 'text']
11:53:45.134 [INFO ] llama_index.core.indices.loading - Loading all indices.
11:53:45.579 [INFO ] private_gpt.ui.ui - Mounting the gradio UI, at path=/
11:53:45.611 [INFO ] uvicorn.error - Started server process [39682]
11:53:45.611 [INFO ] uvicorn.error - Waiting for application startup.
11:53:45.611 [INFO ] uvicorn.error - Application startup complete.
11:53:45.612 [INFO ] uvicorn.error - Uvicorn running on http://0.0.0.0:8001 (Press CTRL+C to quit)

[jaluma]

Are there any more logs or can you explain us what happen when you try to open http://0.0.0.0:8001/?