"Remember to add `--is-embedding` to the command."
"Remember to add `--is-embedding` to the command."
...
@@ -29,30 +29,83 @@
...
@@ -29,30 +29,83 @@
},
},
{
{
"cell_type": "code",
"cell_type": "code",
"execution_count": 7,
"execution_count": 1,
"metadata": {},
"metadata": {},
"outputs": [
"outputs": [
{
{
"name": "stdout",
"name": "stdout",
"output_type": "stream",
"output_type": "stream",
"text": [
"text": [
"Embedding server is ready. Proceeding with the next steps.\n"
"/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n",
"/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n",
" warnings.warn(\n",
"/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n",
"<strong style='color: #00008B;'><br> This cell combines server and notebook output. <br> <br> Typically, the server runs in a separate terminal, <br> but we combine the output of server and notebook to demonstrate the usage better.<br> <br> In our documentation, server output is in gray, notebook output is highlighted.<br> </strong>"
"/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:127: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n",
"in your command line and wait for the server to be ready."
"in your command line and wait for the server to be ready."
...
@@ -34,23 +34,65 @@
...
@@ -34,23 +34,65 @@
"name": "stdout",
"name": "stdout",
"output_type": "stream",
"output_type": "stream",
"text": [
"text": [
"Server is ready. Proceeding with the next steps.\n"
"/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n",
"/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n",
" warnings.warn(\n",
"/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n",
"<strong style='color: #00008B;'><br> Server and notebook outputs are combined for clarity.<br> <br> Typically, the server runs in a separate terminal.<br> <br> Server output is gray; notebook output is highlighted.<br> </strong>"
"print(\"Server is ready. Proceeding with the next steps.\")"
]
]
},
},
{
{
...
@@ -71,7 +113,30 @@
...
@@ -71,7 +113,30 @@
"name": "stdout",
"name": "stdout",
"output_type": "stream",
"output_type": "stream",
"text": [
"text": [
"{\"id\":\"449710eb827c49c99b82ce187e912c2a\",\"object\":\"chat.completion\",\"created\":1729962606,\"model\":\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\"choices\":[{\"index\":0,\"message\":{\"role\":\"assistant\",\"content\":\"LLM stands for Large Language Model. It's a type of artificial intelligence (AI) designed to process and generate human-like language. These models are trained on vast amounts of text data, allowing them to learn patterns, relationships, and context within language.\\n\\nLarge language models use various techniques, such as deep learning and natural language processing, to analyze and understand the input text. They can then use this understanding to generate coherent and context-specific text, such as:\\n\\n1. Responses to questions or prompts\\n2. Summaries of long pieces of text\\n3. Creative writing, like stories or poetry\\n4. Translation of text from one language to another\\n\\nSome popular examples of LLMs include:\\n\\n1. Chatbots: Virtual assistants that can understand and respond to user input\\n2. Virtual assistants: Like Siri, Alexa, or Google Assistant\\n3. Language translation tools: Such as Google Translate\\n4. Writing assistants: Like Grammarly or Language Tool\\n\\nThe key characteristics of LLMs include:\\n\\n1. **Scalability**: They can process large amounts of text data\\n2. **Flexibility**: They can be fine-tuned for specific tasks or domains\\n3. **Contextual understanding**: They can recognize context and nuances in language\\n4. **Creativity**: They can generate original text or responses\\n\\nHowever, LLMs also have limitations and potential drawbacks:\\n\\n1. **Bias**: They can perpetuate existing biases in the training data\\n2. **Misinformation**: They can spread misinformation or false information\\n3. **Dependence on data quality**: The quality of the training data directly affects the model's performance\\n\\nOverall, LLMs are powerful tools that can be used in various applications, from language translation and writing assistance to chatbots and virtual assistants.\"},\"logprobs\":null,\"finish_reason\":\"stop\",\"matched_stop\":128009}],\"usage\":{\"prompt_tokens\":47,\"total_tokens\":408,\"completion_tokens\":361,\"prompt_tokens_details\":null}}"
"{\"id\":\"0635a1c4d1d940f597b11482bed9595f\",\"object\":\"chat.completion\",\"created\":1730261683,\"model\":\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\"choices\":[{\"index\":0,\"message\":{\"role\":\"assistant\",\"content\":\"LLM stands for Large Language Model. It's a type of artificial intelligence (AI) designed to process and understand human language. LLMs are trained on vast amounts of text data, allowing them to learn patterns, relationships, and context within language.\\n\\nLarge language models like myself use natural language processing (NLP) and machine learning algorithms to analyze and generate human-like text. This enables us to:\\n\\n1. **Answer questions**: Provide information on a wide range of topics, from general knowledge to specialized domains.\\n2. **Generate text**: Create coherent and contextually relevant text, such as articles, essays, or even entire stories.\\n3. **Translate languages**: Translate text from one language to another, helping to break language barriers.\\n4. **Summarize content**: Condense long pieces of text into shorter, more digestible summaries.\\n5. **Chat and converse**: Engage in natural-sounding conversations, using context and understanding to respond to questions and statements.\\n\\nLarge language models are typically trained on massive datasets, often consisting of billions of parameters and petabytes of text data. This training enables us to learn complex language patterns, nuances, and context, allowing us to provide helpful and informative responses.\\n\\nSome popular examples of large language models include:\\n\\n1. **BERT (Bidirectional Encoder Representations from Transformers)**: Developed by Google, BERT is a foundational model for many language understanding tasks.\\n2. **RoBERTa (Robustly Optimized BERT Pretraining Approach)**: A variant of BERT, developed by Facebook AI, which improved upon the original model's performance.\\n3. **Transformers**: A family of models developed by Google, which includes BERT and other related architectures.\\n\\nThese models have revolutionized the field of natural language processing and have many exciting applications in areas like:\\n\\n1. **Virtual assistants**: Like Siri, Alexa, or myself, which can understand and respond to voice commands.\\n2. **Language translation**: Enabling real-time translation of languages.\\n3. **Content generation**: Creating original text, such as articles, stories, or even entire books.\\n4. **Customer service**: Providing 24/7 support and answering common customer queries.\\n\\nI hope this helps you understand what a Large Language Model is and its capabilities!\"},\"logprobs\":null,\"finish_reason\":\"stop\",\"matched_stop\":128009}],\"usage\":{\"prompt_tokens\":47,\"total_tokens\":504,\"completion_tokens\":457,\"prompt_tokens_details\":null}}"
]
]
}
}
],
],
...
@@ -100,8 +165,22 @@
...
@@ -100,8 +165,22 @@
"name": "stdout",
"name": "stdout",
"output_type": "stream",
"output_type": "stream",
"text": [
"text": [
"ChatCompletion(id='6bbf20fed17940739eb5cd5d685fa29a', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='Here are 3 countries and their capitals:\\n\\n1. **Country:** Japan\\n**Capital:** Tokyo\\n\\n2. **Country:** Australia\\n**Capital:** Canberra\\n\\n3. **Country:** Brazil\\n**Capital:** Brasília', refusal=None, role='assistant', function_call=None, tool_calls=None), matched_stop=128009)], created=1729962608, model='meta-llama/Meta-Llama-3.1-8B-Instruct', object='chat.completion', service_tier=None, system_fingerprint=None, usage=CompletionUsage(completion_tokens=46, prompt_tokens=49, total_tokens=95, prompt_tokens_details=None))\n"
Chayenne <zhaochenyang@g.ucla.edu>