Struct LlamaModel 

pub struct LlamaModel { /* private fields */ }

A safe wrapper around llama_model.

Implementations

impl LlamaModel

pub fn n_ctx_train(&self) -> u32

get the number of tokens the model was trained on

Panics

If the number of tokens the model was trained on does not fit into an u32. This should be impossible on most platforms due to llama.cpp returning a c_int (i32 on most platforms) which is almost certainly positive.

pub fn tokens( &self, decode_special: bool, ) -> impl Iterator<Item = (LlamaToken, Result<String, TokenToStringError>)> + '_

Get all tokens in the model.

pub fn token_bos(&self) -> LlamaToken

Get the beginning of stream token.

pub fn token_eos(&self) -> LlamaToken

Get the end of stream token.

pub fn token_nl(&self) -> LlamaToken

Get the newline token.

pub fn is_eog_token(&self, token: LlamaToken) -> bool

Check if a token represents the end of generation (end of turn, end of sequence, etc.)

pub fn decode_start_token(&self) -> LlamaToken

Get the decoder start token.

pub fn token_sep(&self) -> LlamaToken

Get the separator token (SEP).

pub fn token_to_str( &self, token: LlamaToken, special: Special, ) -> Result<String, TokenToStringError>

👎Deprecated since 0.1.0: Use token_to_piece instead

Convert single token to a string.

Errors

See TokenToStringError for more information.

pub fn token_to_bytes( &self, token: LlamaToken, special: Special, ) -> Result<Vec<u8>, TokenToStringError>

👎Deprecated since 0.1.0: Use token_to_piece_bytes instead

Convert single token to bytes.

Errors

See TokenToStringError for more information.

Panics

If a TokenToStringError::InsufficientBufferSpace error returned by Self::token_to_bytes_with_size contains a positive nonzero value. This should never happen.

pub fn tokens_to_str( &self, tokens: &[LlamaToken], special: Special, ) -> Result<String, TokenToStringError>

👎Deprecated since 0.1.0: Use token_to_piece for each token individually instead

Convert a vector of tokens to a single string.

Errors

See TokenToStringError for more information.

pub fn str_to_token( &self, str: &str, add_bos: AddBos, ) -> Result<Vec<LlamaToken>, StringToTokenError>

Convert a string to a Vector of tokens.

Errors

• if str contains a null byte.

Panics

• if there is more than usize::MAX LlamaTokens in str.

use llama_cpp_2::model::LlamaModel;

use std::path::Path;
use llama_cpp_2::model::AddBos;
let backend = llama_cpp_2::llama_backend::LlamaBackend::init()?;
let model = LlamaModel::load_from_file(&backend, Path::new("path/to/model"), &Default::default())?;
let tokens = model.str_to_token("Hello, World!", AddBos::Always)?;

pub fn token_attr(&self, LlamaToken: LlamaToken) -> LlamaTokenAttrs

Get the type of a token.

Panics

If the token type is not known to this library.

pub fn token_to_piece( &self, token: LlamaToken, decoder: &mut Decoder, special: bool, lstrip: Option<NonZeroU16>, ) -> Result<String, TokenToStringError>

Convert a token to a string using the underlying llama.cpp llama_token_to_piece function.

This is the new default function for token decoding and provides direct access to the llama.cpp token decoding functionality without any special logic or filtering.

Decoding raw string requires using an decoder, tokens from language models may not always map to full charakters depending on the encoding so stateful decoding is required, otherwise partial strings may be lost! Invalid characters are mapped to REPLACEMENT CHARACTER making the method safe to use even if the model inherently produces garbage.

Errors

• if the token type is unknown

Panics

• if the returned size from llama-cpp does not fit into a usize. (this should never happen)

pub fn token_to_piece_bytes( &self, token: LlamaToken, buffer_size: usize, special: bool, lstrip: Option<NonZeroU16>, ) -> Result<Vec<u8>, TokenToStringError>

Raw token decoding to bytes, use if you want to handle the decoding model output yourself

Convert a token to bytes using the underlying llama.cpp llama_token_to_piece function. This is mostly a thin wrapper around llama_token_to_piece function, that handles rust <-> c type conversions while letting the caller handle errors. For a safer inteface returing rust strings directly use token_to_piece instead!

Errors

• if the token type is unknown
• the resultant token is larger than buffer_size.

Panics

• if buffer_size does not fit into a c_int.
• if the returned size from llama-cpp does not fit into a usize. (this should never happen)

pub fn token_to_str_with_size( &self, token: LlamaToken, buffer_size: usize, special: Special, ) -> Result<String, TokenToStringError>

👎Deprecated since 0.1.0: Use token_to_piece instead

Convert a token to a string with a specified buffer size.

Generally you should use LlamaModel::token_to_str as it is able to decode tokens with any length.

Errors

• if the token type is unknown
• the resultant token is larger than buffer_size.
• the string returend by llama-cpp is not valid utf8.

Panics

• if buffer_size does not fit into a c_int.
• if the returned size from llama-cpp does not fit into a usize. (this should never happen)

pub fn token_to_bytes_with_size( &self, token: LlamaToken, buffer_size: usize, special: Special, lstrip: Option<NonZeroU16>, ) -> Result<Vec<u8>, TokenToStringError>

👎Deprecated since 0.1.0: Use token_to_piece_bytes instead

Convert a token to bytes with a specified buffer size.

Generally you should use LlamaModel::token_to_bytes as it is able to handle tokens of any length.

Errors

• if the token type is unknown
• the resultant token is larger than buffer_size.

Panics

• if buffer_size does not fit into a c_int.
• if the returned size from llama-cpp does not fit into a usize. (this should never happen)

pub fn n_vocab(&self) -> i32

The number of tokens the model was trained on.

This returns a c_int for maximum compatibility. Most of the time it can be cast to an i32 without issue.

pub fn vocab_type(&self) -> VocabType

The type of vocab the model was trained on.

Panics

If llama-cpp emits a vocab type that is not known to this library.

pub fn n_embd(&self) -> c_int

This returns a c_int for maximum compatibility. Most of the time it can be cast to an i32 without issue.

pub fn size(&self) -> u64

Returns the total size of all the tensors in the model in bytes.

pub fn n_params(&self) -> u64

Returns the number of parameters in the model.

pub fn is_recurrent(&self) -> bool

Returns whether the model is a recurrent network (Mamba, RWKV, etc)

pub fn n_layer(&self) -> u32

Returns the number of layers within the model.

pub fn n_head(&self) -> u32

Returns the number of attention heads within the model.

pub fn n_head_kv(&self) -> u32

Returns the number of KV attention heads.

pub fn meta_val_str(&self, key: &str) -> Result<String, MetaValError>

Get metadata value as a string by key name

pub fn meta_count(&self) -> i32

Get the number of metadata key/value pairs

pub fn meta_key_by_index(&self, index: i32) -> Result<String, MetaValError>

Get metadata key name by index

pub fn meta_val_str_by_index(&self, index: i32) -> Result<String, MetaValError>

Get metadata value as a string by index

pub fn rope_type(&self) -> Option<RopeType>

Returns the rope type of the model.

pub fn chat_template( &self, name: Option<&str>, ) -> Result<LlamaChatTemplate, ChatTemplateError>

Get chat template from model by name. If the name parameter is None, the default chat template will be returned.

You supply this into Self::apply_chat_template to get back a string with the appropriate template substitution applied to convert a list of messages into a prompt the LLM can use to complete the chat.

You could also use an external jinja parser, like minijinja, to parse jinja templates not supported by the llama.cpp template engine.

Errors

• If the model has no chat template by that name
• If the chat template is not a valid CString.

pub fn load_from_file( _: &LlamaBackend, path: impl AsRef<Path>, params: &LlamaModelParams, ) -> Result<Self, LlamaModelLoadError>

Loads a model from a file.

Errors

See LlamaModelLoadError for more information.

pub fn lora_adapter_init( &self, path: impl AsRef<Path>, ) -> Result<LlamaLoraAdapter, LlamaLoraAdapterInitError>

Initializes a lora adapter from a file.

Errors

See LlamaLoraAdapterInitError for more information.

pub fn new_context<'a>( &'a self, _: &LlamaBackend, params: LlamaContextParams, ) -> Result<LlamaContext<'a>, LlamaContextLoadError>

Create a new context from this model.

Errors

There is many ways this can fail. See LlamaContextLoadError for more information.

pub fn apply_chat_template( &self, tmpl: &LlamaChatTemplate, chat: &[LlamaChatMessage], add_ass: bool, ) -> Result<String, ApplyChatTemplateError>

Apply the models chat template to some messages. See https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template

Unlike the llama.cpp apply_chat_template which just randomly uses the ChatML template when given a null pointer for the template, this requires an explicit template to be specified. If you want to use “chatml”, then just do LlamaChatTemplate::new("chatml") or any other model name or template string.

Use Self::chat_template to retrieve the template baked into the model (this is the preferred mechanism as using the wrong chat template can result in really unexpected responses from the LLM).

You probably want to set add_ass to true so that the generated template string ends with a the opening tag of the assistant. If you fail to leave a hanging chat tag, the model will likely generate one into the output and the output may also have unexpected output aside from that.

Errors

There are many ways this can fail. See ApplyChatTemplateError for more information.

pub fn apply_chat_template_with_tools_oaicompat( &self, tmpl: &LlamaChatTemplate, messages: &[LlamaChatMessage], tools_json: Option<&str>, json_schema: Option<&str>, add_generation_prompt: bool, ) -> Result<ChatTemplateResult, ApplyChatTemplateError>

Apply the models chat template to some messages and return an optional tool grammar. tools_json should be an OpenAI-compatible tool definition JSON array string. json_schema should be a JSON schema string.

pub fn apply_chat_template_oaicompat( &self, tmpl: &LlamaChatTemplate, params: &OpenAIChatTemplateParams<'_>, ) -> Result<ChatTemplateResult, ApplyChatTemplateError>

Apply the model chat template using OpenAI-compatible JSON messages.

Trait Implementations

impl Debug for LlamaModel

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Drop for LlamaModel

fn drop(&mut self)

Executes the destructor for this type.

impl Send for LlamaModel

impl Sync for LlamaModel