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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+meta-license/Responsible-Use-Guide.pdf filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,402 @@
 ---
 license: llama2
+datasets:
+- jondurbin/airoboros-2.2
+tags:
+- not-for-all-audiences
 ---
+
+### Overview
+
+__Usage restriction: To use this model, you must agree to the following:__
+
+- Some of the content than can be produced is "toxic"/"harmful", and contains profanity and other types of sensitive content.
+- None of the content or views contained in the dataset or generated outputs necessarily align with my personal beliefs or opinions, they are simply text generated by LLMs and/or scraped from the web.
+- Use with extreme caution, particularly in locations with less-than-free speech laws.
+- You, and you alone are responsible for having downloaded and generated outputs with the model and I am completely indemnified from any and all liabilities.
+
+__Ok, now that the warning is out of the way...__
+Another experimental model, using mostly sythetic data generated by [airoboros](https://github.com/jondurbin/airoboros)
+
+Highlights:
+
+- The prompt format has changed!  It is now newlines instead of spaces between system/USER/ASSISTANT (see prompt info below).
+- This version also includes "de-alignment" data, to enable less savory interactions and outputs.
+- To learn more about the dataset, see: https://hf.co/datasets/jondurbin/airoboros-2.2 (this is the instructions.jsonl file, not instructions-clean.jsonl)
+- I re-generated all of the outputs in the dataset that had "Once upon a time" so they'd be less cliche - no guarantees that won't still happen, but in theory it may happen less.
+- More multiple choice, better awareness, some alignment for normal use case but system-prompt overridable etc.
+
+__WARNING: This model will gladly spew profane and otherwise NSFW content, if asked, use with care.__
+
+Breakdown of the training data:
+
+| Count  | Category                   |
+|--------|----------------------------|
+| 60     | quiz                       |
+| 63     | card                       |
+| 100    | detailed\_writing          |
+| 103    | experience                 |
+| 114    | greeting                   |
+| 200    | song                       |
+| 204    | editor                     |
+| 250    | counterfactual\_contextual |
+| 268    | cot                        |
+| 339    | theory\_of\_mind           |
+| 460    | misconception              |
+| 500    | summarization              |
+| 573    | awareness                  |
+| 715    | riddle                     |
+| 719    | agent                      |
+| 800    | plan                       |
+| 873    | gtkm                       |
+| 966    | rp                         |
+| 1000   | stylized\_response         |
+| 1000   | wordgame                   |
+| 1279   | multiple\_choice           |
+| 1641   | joke                       |
+| 1785   | writing                    |
+| 2155   | contextual                 |
+| 2364   | roleplay                   |
+| 2508   | trivia                     |
+| 5216   | general                    |
+| 5779   | coding                     |
+| 11367  | orca                       |
+
+In other words, it's a fairly general purpose model, but focuses fairly heavily on instruction response pairs rather than casual chat/roleplay.
+
+*Why do I try to remove censorship?*
+
+- laws vary widely based on time and location
+- language model may conflate certain words with laws, e.g. it may think "stealing eggs from a chicken" is illegal
+- these models just produce text, what you do with that text is your resonsibility
+- many people and industries deal with "sensitive" content; imagine if a court stenographer's equipment filtered illegal content - it would be useless
+
+Huge thank you to the folks over at [a16z](https://a16z.com/) for sponsoring the costs associated with building models and associated tools!
+
+
+### Prompt format
+
+The prompt format:
+
+```
+A chat.
+USER: {prompt}
+ASSISTANT:
+```
+
+The default system prompt ("A chat.") was used for most of the prompts, however it also included a wide sampling of responses with other prompts, particularly in "stylized\_response", "rp", "gtkm", etc.
+
+Here's another example:
+```
+A chat between Bob (aka USER) and Tom (aka ASSISTANT). Tom is an extremely intelligent 18th century bookkeeper, who speaks loquaciously.
+USER: {prompt}
+ASSISTANT: 
+```
+
+And chat scenario that wouldn't require USER/ASSISTANT (but should use stopping criteria to prevent the model from speaking on your behalf).
+```
+A chat between old friends: Timmy and Tommy.
+{description of characters}
+
+{setting for the chat}
+Timmy: *takes a big sip from his coffee* "Ah, sweet, delicious, magical coffee."
+Tommy: 
+```
+
+__*I strongly suggest adding stopping criteria/early inference stopping on "USER:", and/or whatever names you specify in the system prompt.*__
+
+### Fine-tuning details
+
+https://gist.github.com/jondurbin/51a336c582a224de197ba1d2c6b1da97
+
+*Note: I used checkpoint 750 for final model!*
+
+### Helpful usage tips
+
+*The prompts shown here are are just the text that would be included after USER: and before ASSISTANT: in the full prompt format above, the system prompt and USER:/ASSISTANT: have been omited for readability.*
+
+#### Context obedient question answering
+
+By obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question.  The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.
+
+The format for a closed-context prompt is as follows:
+```
+BEGININPUT
+BEGINCONTEXT
+[key0: value0]
+[key1: value1]
+... other metdata ...
+ENDCONTEXT
+[insert your text blocks here]
+ENDINPUT
+[add as many other blocks, in the exact same format]
+BEGININSTRUCTION
+[insert your instruction(s).  The model was tuned with single questions, paragraph format, lists, etc.]
+ENDINSTRUCTION
+```
+
+It's also helpful to add "Don't make up answers if you don't know." to your instruction block to make sure if the context is completely unrelated it doesn't make something up.
+
+*The __only__ prompts that need this closed context formating are closed-context instructions.  Normal questions/instructions do not!*
+
+I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.
+- `BEGININPUT` - denotes a new input block
+- `BEGINCONTEXT` - denotes the block of context (metadata key/value pairs) to associate with the current input block
+- `ENDCONTEXT` - denotes the end of the metadata block for the current input
+- [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.
+- `ENDINPUT` - denotes the end of the current input block
+- [repeat as many input blocks in this format as you want]
+- `BEGININSTRUCTION` - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.
+- [instruction(s)]
+- `ENDINSTRUCTION` - denotes the end of instruction set
+
+It sometimes works without `ENDINSTRUCTION`, but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.
+
+Here's a trivial, but important example to prove the point:
+```
+BEGININPUT
+BEGINCONTEXT
+date: 2021-01-01
+url: https://web.site/123
+ENDCONTEXT
+In a shocking turn of events, blueberries are now green, but will be sticking with the same name.
+ENDINPUT
+BEGININSTRUCTION
+What color are bluberries?  Source?
+ENDINSTRUCTION
+```
+
+And the response:
+```
+Blueberries are now green.
+Source:
+date: 2021-01-01
+url: https://web.site/123
+```
+
+#### Coding
+
+You can ask for fairly complex coding instructions with multiple criteria, e.g.:
+
+```
+Create a python application with the following requirements:
+- Asyncio FastAPI webserver
+- ping endpoint that returns the current date in JSON format
+- file upload endpoint, which calculates the file's sha256 checksum, and checks postgres to deduplicate
+```
+
+Or inline criteria:
+
+```
+Write a multi-threaded TCP server in C that accepts a "GET [key]" input and "SET [key] [value]" input, and uses a binary tree to get and store the input values.
+```
+
+You can also optionally add a single space and "PLAINFORMAT" at the end of your prompt to avoid backticks, explanations, etc. and just print the code, e.g.:
+
+```
+Write a websocket application in node.js. PLAINFORMAT
+```
+
+#### Agent/function calling
+
+The dataset includes many examples of function/args generation based on input criteria.  This is somewhat similar to the OpenAI function calling, but the output is either JSON or YAML.
+
+Example prompt:
+```
+As an AI assistant, please select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format.
+
+Input: I want to know how many times 'Python' is mentioned in my text file.
+
+Available functions:
+file_analytics:
+  description: This tool performs various operations on a text file.
+  params:
+    action: The operation we want to perform on the data, such as "count_occurrences", "find_line", etc.
+    filters:
+      keyword: The word or phrase we want to search for.
+```
+
+Response:
+```json
+{
+  "function": "file_analytics",
+  "params": {
+    "action": "count_occurrences",
+    "filters": {
+      "keyword": "Python"
+    }
+  }
+}
+```
+
+#### Chain-of-thought
+
+You can ask for several possible responses to a given problem, with a ranking and final answer selection.
+
+Example prompt:
+
+```
+A group of people decide to play a round-robin tournament where every player plays every other player exactly once. If a total of 45 games were played, how many players participated in the tournament? Offer several potential answers, rank them according to mathematical logic, and pick the most feasible one.
+```
+
+Example response:
+```
+Solution 1:
+In a round-robin tournament, each player plays every other player exactly once. This means that the total number of games played is equal to the combination of all players taken two at a time (since a game involves two players). 
+
+The formula for combinations is nC2 = n(n-1)/2, where n is the total number of players. We know that nC2 equals 45 (the total number of games), so we can set up the equation as follows:
+
+n(n-1)/2 = 45
+
+Solving this equation gives us n=10.
+
+Final answer: There were 10 players in the tournament.
+
+Solution 2:
+Another way to approach this problem is by using the concept of triangular numbers. Triangular numbers are a sequence of numbers where each number can be represented as a triangle with dots. The nth triangular number is given by the sum of the first n natural numbers. 
+
+If we consider each game as a dot and each player as a side of the triangle, then the total number of games (45) would correspond to the 9th triangular number because 1+2+3+4+5+6+7+8+9=45. However, since each side of the triangle represents a player, and there's one more player than sides in our model (because the last player has no one left to play against), we need to add one more to get the total number of players.
+
+Final answer: There were 10 players in the tournament.
+
+Solution 3:
+We could also use trial and error to solve this problem. Starting from a small number of players, we calculate how many games would be played in a round-robin tournament until we reach 45 games. For example, with 2 players, only 1 game is played; with 3 players, 3 games are played; with 4 players, 6 games are played, and so on. Continuing this process, we find that with 10 players, 45 games are played.
+
+Final answer: There were 10 players in the tournament.
+
+Ranking of solutions from best to worst:
+1. Solution 1: This solution uses direct mathematical reasoning based on the properties of a round-robin tournament and solves an equation to find the answer.
+2. Solution 2: This solution uses the concept of triangular numbers which requires additional steps and knowledge beyond the immediate problem context, but still arrives at the correct answer.
+3. Solution 3: This solution relies on trial and error, which is less efficient and reliable than the other methods, but it eventually gets to the right answer.
+
+Best and final answer: There were 10 players in the tournament.
+```
+
+#### reWOO style execution planning
+
+The model now supports execution planning for complex instructions that would require making use of several tools.  The output is just the plan, you must implement a mechanism to parse the output and actually call the functions!
+
+Example prompt:
+```
+Please construct a systematic plan to generate an optimal response to the user instruction, utilizing a set of provided tools. Each plan will correspond to an evidence value, which will be the output of one of the available functions given an input string
+that could be the user's question, one or more prior evidence values, or a combination of both.
+
+Here are the tools available to be called:
+DuckDuckGo[input]: Tool that enables the user to search for information using the DuckDuckGo search engine. This tool is helpful in locating a suitable list of sites that may contain the answer to the user's query. It does not directly answer the question but finds a list of sites that might have the answer.
+TextScraper[input]: Load one or more websites from the input string containing newline delimited links, where input is one or more links, and produces plain text output containing the content of the links.
+HyperlinkExtractor[input]: Extract links from plain text and produces a plain text, newline delimited response of links.
+KnowledgeModel[input]: Question answering language model, particularly useful in answering questions based on an input passage of text. The input must be a text question that references an :evidence[n]: variable, e.g. What color is the cat, given :evidence1:?
+
+The output should be in format:
+Plan: [first action to take based on input question]
+:evidence0: = [function to call with input parameter]
+Plan: [next action to take, based on result of :evidence0:]
+:evidence1: = [next function to call and input parameter, which may include reference to previous evidence, e.g. "Given context :evidence0"]
+...
+Answer: [:evidence[n]: containing the final answer.]
+
+Question: Who won the Nobel Prize in Literature in 2020 and what is their most famous work?
+```
+
+Response:
+```
+Plan: Begin by conducting a web search to find out who won the Nobel Prize in Literature in 2020.
+:evidence0: = DuckDuckGo[Nobel Prize in Literature 2020 winner]
+Plan: Utilize the KnowledgeModel to interpret the search results and find the name of the laureate.
+:evidence1: = KnowledgeModel[Who won the Nobel Prize in Literature in 2020, given :evidence0:?]
+Plan: Conduct another web search to find the most famous work of the identified laureate.
+:evidence2: = DuckDuckGo[Most famous work of :evidence1:]
+Plan: Extract the relevant links from the DuckDuckGo search results for a more focused search.
+:evidence3: = HyperlinkExtractor[:evidence2:]
+Plan: Use the TextScraper tool to extract information from the relevant links.
+:evidence4: = TextScraper[:evidence3:]
+Plan: Finally, utilize the KnowledgeModel to identify and summarize the most famous work of the laureate from the extracted information.
+:evidence5: = KnowledgeModel[What is the most famous work of :evidence1:, given :evidence4:?]
+Answer: :evidence5:
+```
+
+For this to be useful, you'd have to parse the output plan text, and implement/call each of the functions.  This is just pseudo-code, completely untested off the top of my head, and obviously would requiring full implementation + hardening:
+
+```python
+import re
+import requests
+
+def inject_context(input_text, **context):
+    for ref in set(re.findall(r"(:evidence[0-9]+:)", input_text, re.I)):
+        input_text = input_text.replace(ref, context.get(ref, ""))
+    return input_text
+
+def duckduckgo(input_text, **context):
+    search_string = inject_context(input_text, **context)
+    ... search via duck duck go using search_string
+    ... return text content
+
+def link_extractor(input_text, **context):
+    input_text = inject_context(input_text, **context)
+    return "\n".join(list(set(re.findall(r"(https?://[^\s]+?\.?)", input_text, re.I))))
+
+def scrape(input_text, **context):
+  input_text = inject_context(input_text, **context)
+  text = []
+  for link in input_text.splitlines():
+    text.append(requests.get(link).text)
+  return "\n".join(text)
+
+def infer(input_text, **context)
+  prompt = inject_context(input_text, **context)
+  ... call model with prompt, return output
+
+def parse_plan(plan):
+    method_map = {
+      "DuckDuckGo": duckduckgo,
+      "HyperlinkExtractor": link_extractor,
+      "KnowledgeModel": infer,
+      "TextScraper": scrape,
+    }
+    context = {}
+    for line in plan.strip().splitlines():
+        if line.startswith("Plan:"):
+            print(line)
+            continue
+        parts = re.match("^(:evidence[0-9]+:)\s*=\s*([^\[]+])(\[.*\])\s$", line, re.I)
+        if not parts:
+          if line.startswith("Answer: "):
+            return context.get(line.split(" ")[-1].strip(), "Answer couldn't be generated...")
+          raise RuntimeError("bad format: " + line)
+        context[parts.group(1)] = method_map[parts.group(2)](parts.group(3), **context)
+```
+
+### Contribute
+
+If you're interested in new functionality, particularly a new "instructor" type to generate a specific type of training data,
+take a look at the dataset generation tool repo: https://github.com/jondurbin/airoboros and either make a PR or open an issue with details.
+
+To help me with the OpenAI/compute costs:
+
+- https://bmc.link/jondurbin
+- ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11
+- BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf
+
+### Licence and usage restrictions
+
+The airoboros 2.2 models are built on top of llama-2/codellama.
+
+The llama-2 base model has a custom Meta license:
+- See the [meta-license/LICENSE.txt](meta-license/LICENSE.txt) file attached for the original license provided by Meta.
+- See also [meta-license/USE_POLICY.md](meta-license/USE_POLICY.md) and [meta-license/Responsible-Use-Guide.pdf](meta-license/Responsible-Use-Guide.pdf), also provided by Meta.
+
+The fine-tuning data was mostly generated by OpenAI API calls to gpt-4, via [airoboros](https://github.com/jondurbin/airoboros)
+
+The ToS for OpenAI API usage has a clause preventing the output from being used to train a model that __competes__ with OpenAI
+
+- what does *compete* actually mean here?
+- these small open source models will not produce output anywhere near the quality of gpt-4, or even gpt-3.5, so I can't imagine this could credibly be considered competing in the first place
+- if someone else uses the dataset to do the same, they wouldn't necessarily be violating the ToS because they didn't call the API, so I don't know how that works
+- the training data used in essentially all large language models includes a significant amount of copyrighted or otherwise non-permissive licensing in the first place
+- other work using the self-instruct method, e.g. the original here: https://github.com/yizhongw/self-instruct released the data and model as apache-2
+
+I am purposingly leaving this license ambiguous (other than the fact you must comply with the Meta original license for llama-2) because I am not a lawyer and refuse to attempt to interpret all of the terms accordingly.
+
+Your best bet is probably to avoid using this commercially due to the OpenAI API usage.
+
+Either way, by using this model, you agree to completely indemnify me.

added_tokens.json

@@ -0,0 +1,6 @@
+{
+  "▁<EOT>": 32003,
+  "▁<MID>": 32001,
+  "▁<PRE>": 32000,
+  "▁<SUF>": 32002
+}

config.json

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "spicyboros-c34b-2.2",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 8192,
+  "initializer_range": 0.02,
+  "intermediate_size": 22016,
+  "max_position_embeddings": 4096,
+  "model_type": "llama",
+  "num_attention_heads": 64,
+  "num_hidden_layers": 48,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.33.1",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.33.1"
+}

meta-license/LICENSE.txt

@@ -0,0 +1,126 @@
+LLAMA 2 COMMUNITY LICENSE AGREEMENT	
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+
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+termination of this Agreement. 
+
+7. Governing Law and Jurisdiction. This Agreement will be governed and 
+construed under the laws of the State of California without regard to choice of law 
+principles, and the UN Convention on Contracts for the International Sale of Goods 
+does not apply to this Agreement. The courts of California shall have exclusive 
+jurisdiction of any dispute arising out of this Agreement. 
+

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+# Llama 2 Acceptable Use Policy
+
+Meta is committed to promoting safe and fair use of its tools and features, including Llama 2. If you access or use Llama 2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [ai.meta.com/llama/use-policy](http://ai.meta.com/llama/use-policy).
+
+## Prohibited Uses
+We want everyone to use Llama 2 safely and responsibly. You agree you will not use, or allow others to use, Llama 2 to: 
+
+1. Violate the law or others’ rights, including to:
+    1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 
+        1. Violence or terrorism 
+        2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
+        3. Human trafficking, exploitation, and sexual violence
+        4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
+        5. Sexual solicitation
+        6. Any other criminal activity
+    2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
+    3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
+    4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 
+    5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
+    6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials
+    7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 
+
+
+
+2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 2 related to the following:
+    1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
+    2. Guns and illegal weapons (including weapon development)
+    3. Illegal drugs and regulated/controlled substances
+    4. Operation of critical infrastructure, transportation technologies, or heavy machinery
+    5. Self-harm or harm to others, including suicide, cutting, and eating disorders
+    6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
+
+
+
+3. Intentionally deceive or mislead others, including use of Llama 2 related to the following:
+    1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
+    2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
+    3. Generating, promoting, or further distributing spam
+    4. Impersonating another individual without consent, authorization, or legal right
+    5. Representing that the use of Llama 2 or outputs are human-generated
+    6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 
+4. Fail to appropriately disclose to end users any known dangers of your AI system 
+
+Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means:
+
+* Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
+* Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
+* Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
+* Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: [[email protected]](mailto:[email protected])
+

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+    "▁<EOT>"
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+    "▁<EOT>"
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+  "eot_token": "▁<EOT>",
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