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docs: Fix README indentation

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Andrei Betlen 2023-11-27 18:29:13 -05:00
parent 1539146a5e
commit 41428244f0
1 changed files with 48 additions and 48 deletions
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README.md
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@ -116,11 +116,11 @@ Below is a short example demonstrating how to use the high-level API to for basi
>>> from llama_cpp import Llama >>> from llama_cpp import Llama
>>> llm = Llama(model_path="./models/7B/llama-model.gguf") >>> llm = Llama(model_path="./models/7B/llama-model.gguf")
>>> output = llm( >>> output = llm(
"Q: Name the planets in the solar system? A: ", # Prompt "Q: Name the planets in the solar system? A: ", # Prompt
max_tokens=32, # Generate up to 32 tokens max_tokens=32, # Generate up to 32 tokens
stop=["Q:", "\n"], # Stop generating just before the model would generate a new question stop=["Q:", "\n"], # Stop generating just before the model would generate a new question
echo=True # Echo the prompt back in the output echo=True # Echo the prompt back in the output
) ) # Generate a completion, can also call create_completion
>>> print(output) >>> print(output)
{ {
"id": "cmpl-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx", "id": "cmpl-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx",
@ -153,13 +153,13 @@ Note that `chat_format` option must be set for the particular model you are usin
>>> from llama_cpp import Llama >>> from llama_cpp import Llama
>>> llm = Llama(model_path="path/to/llama-2/llama-model.gguf", chat_format="llama-2") >>> llm = Llama(model_path="path/to/llama-2/llama-model.gguf", chat_format="llama-2")
>>> llm.create_chat_completion( >>> llm.create_chat_completion(
messages = [ messages = [
{"role": "system", "content": "You are an assistant who perfectly describes images."}, {"role": "system", "content": "You are an assistant who perfectly describes images."},
{ {
"role": "user", "role": "user",
"content": "Describe this image in detail please." "content": "Describe this image in detail please."
} }
] ]
) )
``` ```
@ -175,43 +175,43 @@ The gguf-converted files for this model can be found here: [functionary-7b-v1](h
>>> from llama_cpp import Llama >>> from llama_cpp import Llama
>>> llm = Llama(model_path="path/to/functionary/llama-model.gguf", chat_format="functionary") >>> llm = Llama(model_path="path/to/functionary/llama-model.gguf", chat_format="functionary")
>>> llm.create_chat_completion( >>> llm.create_chat_completion(
messages = [ messages = [
{ {
"role": "system", "role": "system",
"content": "A chat between a curious user and an artificial intelligence assitant. The assistant gives helpful, detailed, and polite answers to the user's questions. The assistant callse functions with appropriate input when necessary" "content": "A chat between a curious user and an artificial intelligence assitant. The assistant gives helpful, detailed, and polite answers to the user's questions. The assistant callse functions with appropriate input when necessary"
}, },
{ {
"role": "user", "role": "user",
"content": "Extract Jason is 25 years old" "content": "Extract Jason is 25 years old"
}
],
tools=[{
"type": "function",
"function": {
"name": "UserDetail",
"parameters": {
"type": "object"
"title": "UserDetail",
"properties": {
"name": {
"title": "Name",
"type": "string"
},
"age": {
"title": "Age",
"type": "integer"
}
},
"required": [ "name", "age" ]
} }
} ],
}], tools=[{
tool_choices=[{ "type": "function",
"type": "function", "function": {
"function": { "name": "UserDetail",
"name": "UserDetail" "parameters": {
} "type": "object"
}] "title": "UserDetail",
"properties": {
"name": {
"title": "Name",
"type": "string"
},
"age": {
"title": "Age",
"type": "integer"
}
},
"required": [ "name", "age" ]
}
}
}],
tool_choices=[{
"type": "function",
"function": {
"name": "UserDetail"
}
}]
) )
``` ```