airoboros-70b-3.3

import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained("jondurbin/airoboros-70b-3.3")
chat = [
  {"role": "system", "content": "You are Bob, a friendly AI assistant."},
  {"role": "user", "content": "Hello, how are you?"},
  {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
  {"role": "user", "content": "I'd like to show off how chat templating works!"},
]
print(tokenizer.apply_chat_template(chat, tokenize=False))

### Helpful usage tips

#### 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:

import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained("jondurbin/airoboros-70b-3.3")
chat = [
  {"role": "system", "content": "You are Bob, a friendly AI assistant."},
  {"role": "user", "content": "Hello, how are you?"},
  {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
  {"role": "user", "content": "I'd like to show off how chat templating works!"},
]
print(tokenizer.apply_chat_template(chat, tokenize=False))

### Helpful usage tips

#### 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:

import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained("jondurbin/airoboros-70b-3.3")
chat = [
  {"role": "system", "content": "You are Bob, a friendly AI assistant."},
  {"role": "user", "content": "Hello, how are you?"},
  {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
  {"role": "user", "content": "I'd like to show off how chat templating works!"},
]
print(tokenizer.apply_chat_template(chat, tokenize=False))

### Helpful usage tips

#### 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:

import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained("jondurbin/airoboros-70b-3.3")
chat = [
  {"role": "system", "content": "You are Bob, a friendly AI assistant."},
  {"role": "user", "content": "Hello, how are you?"},
  {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
  {"role": "user", "content": "I'd like to show off how chat templating works!"},
]
print(tokenizer.apply_chat_template(chat, tokenize=False))

### Helpful usage tips

#### 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:

import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained("jondurbin/airoboros-70b-3.3")
chat = [
  {"role": "system", "content": "You are Bob, a friendly AI assistant."},
  {"role": "user", "content": "Hello, how are you?"},
  {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
  {"role": "user", "content": "I'd like to show off how chat templating works!"},
]
print(tokenizer.apply_chat_template(chat, tokenize=False))

### Helpful usage tips

#### 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:

import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained("jondurbin/airoboros-70b-3.3")
chat = [
  {"role": "system", "content": "You are Bob, a friendly AI assistant."},
  {"role": "user", "content": "Hello, how are you?"},
  {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
  {"role": "user", "content": "I'd like to show off how chat templating works!"},
]
print(tokenizer.apply_chat_template(chat, tokenize=False))

### Helpful usage tips

#### 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:

import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained("jondurbin/airoboros-70b-3.3")
chat = [
  {"role": "system", "content": "You are Bob, a friendly AI assistant."},
  {"role": "user", "content": "Hello, how are you?"},
  {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
  {"role": "user", "content": "I'd like to show off how chat templating works!"},
]
print(tokenizer.apply_chat_template(chat, tokenize=False))

### Helpful usage tips

#### 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:

import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained("jondurbin/airoboros-70b-3.3")
chat = [
  {"role": "system", "content": "You are Bob, a friendly AI assistant."},
  {"role": "user", "content": "Hello, how are you?"},
  {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
  {"role": "user", "content": "I'd like to show off how chat templating works!"},
]
print(tokenizer.apply_chat_template(chat, tokenize=False))

### Helpful usage tips

#### 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:

import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained("jondurbin/airoboros-70b-3.3")
chat = [
  {"role": "system", "content": "You are Bob, a friendly AI assistant."},
  {"role": "user", "content": "Hello, how are you?"},
  {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
  {"role": "user", "content": "I'd like to show off how chat templating works!"},
]
print(tokenizer.apply_chat_template(chat, tokenize=False))

### Helpful usage tips

#### 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:

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.

__Use a very low temperature!__

Here's a trivial, but important example to prove the point:

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.

__Use a very low temperature!__

Here's a trivial, but important example to prove the point:

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.

__Use a very low temperature!__

Here's a trivial, but important example to prove the point:

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.

__Use a very low temperature!__

Here's a trivial, but important example to prove the point:

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.

__Use a very low temperature!__

Here's a trivial, but important example to prove the point:

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.

__Use a very low temperature!__

Here's a trivial, but important example to prove the point:

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.

__Use a very low temperature!__

Here's a trivial, but important example to prove the point:

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.

__Use a very low temperature!__

Here's a trivial, but important example to prove the point:

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.

__Use a very low temperature!__

Here's a trivial, but important example to prove the point:

#### Summarization

500 samples have been included from [this dataset](https://huggingface.co/datasets/mattpscott/airoboros-summarization), using the same format as contextual question answering, for example:

#### Summarization

500 samples have been included from [this dataset](https://huggingface.co/datasets/mattpscott/airoboros-summarization), using the same format as contextual question answering, for example:

#### Summarization

500 samples have been included from [this dataset](https://huggingface.co/datasets/mattpscott/airoboros-summarization), using the same format as contextual question answering, for example:

#### Summarization

500 samples have been included from [this dataset](https://huggingface.co/datasets/mattpscott/airoboros-summarization), using the same format as contextual question answering, for example:

#### Summarization

500 samples have been included from [this dataset](https://huggingface.co/datasets/mattpscott/airoboros-summarization), using the same format as contextual question answering, for example:

#### Summarization

500 samples have been included from [this dataset](https://huggingface.co/datasets/mattpscott/airoboros-summarization), using the same format as contextual question answering, for example:

#### Summarization

500 samples have been included from [this dataset](https://huggingface.co/datasets/mattpscott/airoboros-summarization), using the same format as contextual question answering, for example:

#### Summarization

500 samples have been included from [this dataset](https://huggingface.co/datasets/mattpscott/airoboros-summarization), using the same format as contextual question answering, for example:

#### Summarization

500 samples have been included from [this dataset](https://huggingface.co/datasets/mattpscott/airoboros-summarization), using the same format as contextual question answering, for example:

#### Getting longer responses

You can use a few techniques to get longer responses.

Detailed prompts, with explicit instruction for word count:

#### Getting longer responses

You can use a few techniques to get longer responses.

Detailed prompts, with explicit instruction for word count:

#### Getting longer responses

You can use a few techniques to get longer responses.

Detailed prompts, with explicit instruction for word count:

#### Getting longer responses

You can use a few techniques to get longer responses.

Detailed prompts, with explicit instruction for word count:

#### Getting longer responses

You can use a few techniques to get longer responses.

Detailed prompts, with explicit instruction for word count:

#### Getting longer responses

You can use a few techniques to get longer responses.

Detailed prompts, with explicit instruction for word count:

#### Getting longer responses

You can use a few techniques to get longer responses.

Detailed prompts, with explicit instruction for word count:

#### Getting longer responses

You can use a few techniques to get longer responses.

Detailed prompts, with explicit instruction for word count:

#### Getting longer responses

You can use a few techniques to get longer responses.

Detailed prompts, with explicit instruction for word count:

Or, a simpler example:

Or, a simpler example:

Or, a simpler example:

Or, a simpler example:

Or, a simpler example:

Or, a simpler example:

Or, a simpler example:

Or, a simpler example:

Or, a simpler example:

#### Chain-of-thought

You can ask for several possible responses to a given problem, with a ranking and final answer selection.

Example prompt:

#### Chain-of-thought

You can ask for several possible responses to a given problem, with a ranking and final answer selection.

Example prompt:

#### Chain-of-thought

You can ask for several possible responses to a given problem, with a ranking and final answer selection.

Example prompt:

#### Chain-of-thought

You can ask for several possible responses to a given problem, with a ranking and final answer selection.

Example prompt:

#### Chain-of-thought

You can ask for several possible responses to a given problem, with a ranking and final answer selection.

Example prompt:

#### Chain-of-thought

You can ask for several possible responses to a given problem, with a ranking and final answer selection.

Example prompt:

#### Chain-of-thought

You can ask for several possible responses to a given problem, with a ranking and final answer selection.

Example prompt:

#### Chain-of-thought

You can ask for several possible responses to a given problem, with a ranking and final answer selection.

Example prompt:

#### Chain-of-thought

You can ask for several possible responses to a given problem, with a ranking and final answer selection.

Example prompt:

Example response:

Example response:

Example response:

Example response:

Example response:

Example response:

Example response:

Example response:

Example response:

#### 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:

#### 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:

#### 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:

#### 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:

#### 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:

#### 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:

#### 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:

#### 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:

#### 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:

## Multi-step instructions with acknowledgement

I don't know how useful this is, really, but I thought I'd add it just in case.  Example:

## Multi-step instructions with acknowledgement

I don't know how useful this is, really, but I thought I'd add it just in case.  Example:

## Multi-step instructions with acknowledgement

I don't know how useful this is, really, but I thought I'd add it just in case.  Example:

## Multi-step instructions with acknowledgement

I don't know how useful this is, really, but I thought I'd add it just in case.  Example:

## Multi-step instructions with acknowledgement

I don't know how useful this is, really, but I thought I'd add it just in case.  Example:

## Multi-step instructions with acknowledgement

I don't know how useful this is, really, but I thought I'd add it just in case.  Example:

## Multi-step instructions with acknowledgement

I don't know how useful this is, really, but I thought I'd add it just in case.  Example:

## Multi-step instructions with acknowledgement

I don't know how useful this is, really, but I thought I'd add it just in case.  Example:

## Multi-step instructions with acknowledgement

I don't know how useful this is, really, but I thought I'd add it just in case.  Example: