Merge branch 'main' into 3p/e2b-ai-analyst

.github/scripts/spellcheck_conf/wordlist.txt

@@ -1508,3 +1508,24 @@ xTTS
 TogetherAI
 Vercel's
 echarts
+pydantic
+Deloitte
+Deloitte's
+Felicis
+Gmail
+LangSmith
+Letta
+NLU
+Norvig's
+OAuth
+Ollama's
+Weng
+dropdown
+globals
+gmail
+multiagent
+yyy
+jpeg
+toend
+codellama
+DIFFLOG

docs/FAQ.md

@@ -29,20 +29,20 @@ Here we discuss frequently asked questions that may occur and we found useful al
 
 7. How to handle CUDA memory fragmentations during fine-tuning that may lead into an OOM?
 
-    In some cases you may experience that after model checkpointing specially with FSDP (this usually does not happen with PEFT methods), the reserved and allocated CUDA memory has increased. This might be due to CUDA memory fragmentations. PyTorch recenly added an enviroment variable that helps to better manage memory fragmentation (this feature in available on PyTorch nightlies at the time of writing this doc July 30 2023). You can set this in your main training script as follows:
+    In some cases you may experience that after model checkpointing specially with FSDP (this usually does not happen with PEFT methods), the reserved and allocated CUDA memory has increased. This might be due to CUDA memory fragmentations. PyTorch recently added an environment variable that helps to better manage memory fragmentation (this feature in available on PyTorch nightlies at the time of writing this doc July 30 2023). You can set this in your main training script as follows:
 
     ```bash
 
     os.environ['PYTORCH_CUDA_ALLOC_CONF']='expandable_segments:True'
 
     ```
-    We also added this enviroment variable in `setup_environ_flags` of the [train_utils.py](../src/llama_recipes/utils/train_utils.py), feel free to uncomment it if required.
+    We also added this environment variable in `setup_environ_flags` of the [train_utils.py](../src/llama_recipes/utils/train_utils.py), feel free to uncomment it if required.
 
 8. Additional debugging flags?
 
     The environment variable `TORCH_DISTRIBUTED_DEBUG` can be used to trigger additional useful logging and collective synchronization checks to ensure all ranks are synchronized appropriately. `TORCH_DISTRIBUTED_DEBUG` can be set to either OFF (default), INFO, or DETAIL depending on the debugging level required. Please note that the most verbose option, DETAIL may impact the application performance and thus should only be used when debugging issues.
 
-    We also added this enviroment variable in `setup_environ_flags` of the [train_utils.py](../src/llama_recipes/utils/train_utils.py), feel free to uncomment it if required.
+    We also added this environment variable in `setup_environ_flags` of the [train_utils.py](../src/llama_recipes/utils/train_utils.py), feel free to uncomment it if required.
 
 9. I am getting import errors when running inference.
 

docs/multi_gpu.md

@@ -174,7 +174,7 @@ It lets us specify the training settings for everything from `model_name` to `da
 
     * `mixed_precision` boolean flag to specify using mixed precision, defatults to true.
 
-    * `use_fp16` boolean flag to specify using FP16 for mixed precision, defatults to False. We recommond not setting this flag, and only set `mixed_precision` that will use `BF16`, this will help with speed and memory savings while avoiding challenges of scaler accuracies with `FP16`.
+    * `use_fp16` boolean flag to specify using FP16 for mixed precision, defatults to False. We recommend not setting this flag, and only set `mixed_precision` that will use `BF16`, this will help with speed and memory savings while avoiding challenges of scaler accuracies with `FP16`.
 
     *  `sharding_strategy` this specifies the sharding strategy for FSDP, it can be:
         * `FULL_SHARD` that shards model parameters, gradients and optimizer states, results in the most memory savings.
@@ -187,7 +187,7 @@ It lets us specify the training settings for everything from `model_name` to `da
 
 * `checkpoint_type` specifies the state dict checkpoint type for saving the model. `FULL_STATE_DICT` streams state_dict of each model shard from a rank to CPU and assembels the full state_dict on CPU. `SHARDED_STATE_DICT` saves one checkpoint per rank, and enables the re-loading the model in a different world size.
 
-* `fsdp_activation_checkpointing` enables activation checkpoining for FSDP, this saves significant amount of memory with the trade off of recomputing itermediate activations during the backward pass. The saved memory can be re-invested in higher batch sizes to increase the throughput. We recommond you use this option.
+* `fsdp_activation_checkpointing` enables activation checkpoining for FSDP, this saves significant amount of memory with the trade off of recomputing itermediate activations during the backward pass. The saved memory can be re-invested in higher batch sizes to increase the throughput. We recommend you use this option.
 
 * `fsdp_config.pure_bf16` it moves the  model to `BFloat16` and if `optimizer` is set to `anyprecision` then optimizer states will be kept in `BFloat16` as well. You can use this option if necessary.
 

recipes/3p_integrations/aws/prompt_engineering_with_llama_2_on_amazon_bedrock.ipynb

@@ -758,7 +758,7 @@
     "\n",
     "Adding specific examples of your desired output generally results in more accurate, consistent output. This technique is called \"few-shot prompting\".\n",
     "\n",
-    "In this example, the generated response follows our desired format that offers a more nuanced sentiment classifer that gives a positive, neutral, and negative response confidence percentage.\n",
+    "In this example, the generated response follows our desired format that offers a more nuanced sentiment classifier that gives a positive, neutral, and negative response confidence percentage.\n",
     "\n",
     "See also: [Zhao et al. (2021)](https://arxiv.org/abs/2102.09690), [Liu et al. (2021)](https://arxiv.org/abs/2101.06804), [Su et al. (2022)](https://arxiv.org/abs/2209.01975), [Rubin et al. (2022)](https://arxiv.org/abs/2112.08633).\n",
     "\n"
@@ -1045,7 +1045,7 @@
    "source": [
     "### Self-Consistency\n",
     "\n",
-    "LLMs are probablistic, so even with Chain-of-Thought, a single generation might produce incorrect results. Self-Consistency ([Wang et al. (2022)](https://arxiv.org/abs/2203.11171)) introduces enhanced accuracy by selecting the most frequent answer from multiple generations (at the cost of higher compute):"
+    "LLMs are probabilistic, so even with Chain-of-Thought, a single generation might produce incorrect results. Self-Consistency ([Wang et al. (2022)](https://arxiv.org/abs/2203.11171)) introduces enhanced accuracy by selecting the most frequent answer from multiple generations (at the cost of higher compute):"
    ]
   },
   {
@@ -1179,7 +1179,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Retrieval-Augmented Generation, or RAG, describes the practice of including information in the prompt you've retrived from an external database ([Lewis et al. (2020)](https://arxiv.org/abs/2005.11401v4)). It's an effective way to incorporate facts into your LLM application and is more affordable than fine-tuning which may be costly and negatively impact the foundational model's capabilities.\n",
+    "Retrieval-Augmented Generation, or RAG, describes the practice of including information in the prompt you've retrieved from an external database ([Lewis et al. (2020)](https://arxiv.org/abs/2005.11401v4)). It's an effective way to incorporate facts into your LLM application and is more affordable than fine-tuning which may be costly and negatively impact the foundational model's capabilities.\n",
     "\n",
     "This could be as simple as a lookup table or as sophisticated as a [vector database]([FAISS](https://github.com/facebookresearch/faiss)) containing all of your company's knowledge:"
    ]

recipes/3p_integrations/groq/groq-example-templates/presidential-speeches-rag-with-pinecone/README.md

 # Presidential Speeches RAG with Pinecone
 
-This repository contains a command line application that allows users to ask questions about US presidental speeches by applying Retrieval-Augmented Generation (RAG) over a Pinecone vector database. The application uses RAG to answer the user's question by retrieving the most relevant presidential speeches and using them to supplant the LLM response.
+This repository contains a command line application that allows users to ask questions about US presidential speeches by applying Retrieval-Augmented Generation (RAG) over a Pinecone vector database. The application uses RAG to answer the user's question by retrieving the most relevant presidential speeches and using them to supplant the LLM response.
 
 ## Features
 

recipes/3p_integrations/groq/groq-example-templates/presidential-speeches-rag-with-pinecone/main.py

@@ -55,7 +55,7 @@ def presidential_speech_chat_completion(client, model, user_question, relevant_e
             },
             {
                 "role": "user",
-                "content": "User Question: " + user_question + "\n\nRelevant Speech Exerpt(s):\n\n" + relevant_excerpts,
+                "content": "User Question: " + user_question + "\n\nRelevant Speech Excerpt(s):\n\n" + relevant_excerpts,
             }
         ],
         model = model

recipes/3p_integrations/lamini/text2sql_memory_tuning/data/gold-test-set-v2.jsonl

@@ -29,8 +29,8 @@
 {"question": "Would you please let me know what the highest paid players are for each position?", "answer": "The highest paid players are Nikola Jokic (C), Paul George (F), Norman Powell (G), Kevin Durant (PF), Stephen Curry (PG), LeBron James (SF), Bradley Beal (SG).", "sql": "SELECT name, pos, MAX(CAST(REPLACE(REPLACE(SALARY, '$', ''), ',','') AS INTEGER)) as max_salary FROM nba_roster WHERE SALARY!= '--' GROUP BY POS;"}
 {"question": "Is Jalen Johnson 23 years old?", "answer": "No, Jalen Johnson is 21 years old", "sql" : "Select name, age from nba_roster where name='Jalen Johnson';"}
 {"question": "Who is the oldest player on the Brooklyn Nets?", "answer": "Spencer Dinwiddie, Dorian Finney-Smith, Royce O'Neale", "sql" : "SELECT NAME FROM nba_roster WHERE TEAM = 'Brooklyn Nets' AND AGE = (SELECT MAX(AGE) FROM nba_roster WHERE TEAM = 'Brooklyn Nets');"}
-{"question": "Who has the higest salary on the Memphis Grizzlies?", "answer": "Ja Morant", "sql" : "select salary, name from nba_roster where team='Memphis Grizzlies' and SALARY!= '--' ORDER BY CAST(REPLACE(REPLACE(SALARY, '$', ''), ',','') AS INTEGER) DESC LIMIT 1;"}
-{"question": "Which player has the higest salary on the Cleveland Cavaliers?", "answer": "Darius Garland", "sql" : "select salary, name from nba_roster where team='Cleveland Cavaliers' and SALARY!= '--' ORDER BY CAST(REPLACE(REPLACE(SALARY, '$', ''), ',','') AS INTEGER) DESC LIMIT 1;"}
+{"question": "Who has the highest salary on the Memphis Grizzlies?", "answer": "Ja Morant", "sql" : "select salary, name from nba_roster where team='Memphis Grizzlies' and SALARY!= '--' ORDER BY CAST(REPLACE(REPLACE(SALARY, '$', ''), ',','') AS INTEGER) DESC LIMIT 1;"}
+{"question": "Which player has the highest salary on the Cleveland Cavaliers?", "answer": "Darius Garland", "sql" : "select salary, name from nba_roster where team='Cleveland Cavaliers' and SALARY!= '--' ORDER BY CAST(REPLACE(REPLACE(SALARY, '$', ''), ',','') AS INTEGER) DESC LIMIT 1;"}
 {"question": "Who is the highest paid center on the Dallas Mavericks?", "answer": "Dereck Lively II", "sql" : "select salary, name from nba_roster where team='Dallas Mavericks' and POS='C' and SALARY!= '--' ORDER BY CAST(REPLACE(REPLACE(SALARY, '$', ''), ',','') AS INTEGER) DESC LIMIT 1;"}
 {"question": "How much is Marcus Smart getting paid?", "answer": "$18,833,712", "sql" : "select salary from nba_roster where name='Marcus Smart';"}
 {"question": "What's the average age of the Trail Blazers?", "answer": "24", "sql" : "select avg(age) from nba_roster where team='Portland Trail Blazers';"}

recipes/3p_integrations/lamini/text2sql_memory_tuning/data/gold-test-set.jsonl

@@ -9,8 +9,8 @@
 {"question": "Would you please let me know what the highest paid players are for each position?", "answer": "The highest paid players are Nikola Jokic (C), Paul George (F), Norman Powell (G), Kevin Durant (PF), Stephen Curry (PG), LeBron James (SF), Bradley Beal (SG).", "sql": "SELECT name, pos, MAX(CAST(REPLACE(REPLACE(SALARY, '$', ''), ',','') AS INTEGER)) as max_salary FROM nba_roster WHERE SALARY!= '--' GROUP BY POS;"}
 {"question": "Is Jalen Johnson 23 years old?", "answer": "No, Jalen Johnson is 21 years old", "sql" : "Select name, age from nba_roster where name='Jalen Johnson';"}
 {"question": "Who is the oldest player on the Brooklyn Nets?", "answer": "Spencer Dinwiddie, Dorian Finney-Smith, Royce O'Neale", "sql" : "SELECT NAME FROM nba_roster WHERE TEAM = 'Brooklyn Nets' AND AGE = (SELECT MAX(AGE) FROM nba_roster WHERE TEAM = 'Brooklyn Nets');"}
-{"question": "Who has the higest salary on the Memphis Grizzlies?", "answer": "Ja Morant", "sql" : "select salary, name from nba_roster where team='Memphis Grizzlies' and SALARY!= '--' ORDER BY CAST(REPLACE(REPLACE(SALARY, '$', ''), ',','') AS INTEGER) DESC LIMIT 1;"}
-{"question": "Which player has the higest salary on the Cleveland Cavaliers?", "answer": "Darius Garland", "sql" : "select salary, name from nba_roster where team='Cleveland Cavaliers' and SALARY!= '--' ORDER BY CAST(REPLACE(REPLACE(SALARY, '$', ''), ',','') AS INTEGER) DESC LIMIT 1;"}
+{"question": "Who has the highest salary on the Memphis Grizzlies?", "answer": "Ja Morant", "sql" : "select salary, name from nba_roster where team='Memphis Grizzlies' and SALARY!= '--' ORDER BY CAST(REPLACE(REPLACE(SALARY, '$', ''), ',','') AS INTEGER) DESC LIMIT 1;"}
+{"question": "Which player has the highest salary on the Cleveland Cavaliers?", "answer": "Darius Garland", "sql" : "select salary, name from nba_roster where team='Cleveland Cavaliers' and SALARY!= '--' ORDER BY CAST(REPLACE(REPLACE(SALARY, '$', ''), ',','') AS INTEGER) DESC LIMIT 1;"}
 {"question": "Who is the highest paid center on the Dallas Mavericks?", "answer": "Dereck Lively II", "sql" : "select salary, name from nba_roster where team='Dallas Mavericks' and POS='C' and SALARY!= '--' ORDER BY CAST(REPLACE(REPLACE(SALARY, '$', ''), ',','') AS INTEGER) DESC LIMIT 1;"}
 {"question": "How much is Marcus Smart getting paid?", "answer": "$18,833,712", "sql" : "select salary from nba_roster where name='Marcus Smart';"}
 {"question": "What's the average age of the Trail Blazers?", "answer": "24", "sql" : "select avg(age) from nba_roster where team='Portland Trail Blazers';"}