jhqxxx
73 lines
2.6 KiB
Rust
73 lines
2.6 KiB
Rust
use crate::models::common::generate::{GenerationDataProvider, PrepareData};
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use crate::{
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chat_template::ChatTemplate,
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models::lfm2::{
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config::{Lfm2Config, Lfm2GenerateConfig},
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model::Lfm2Model,
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},
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tokenizer::TokenizerModel,
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utils::{find_type_files, get_device, get_dtype},
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};
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use anyhow::Result;
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use candle_core::{DType, Device};
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use candle_nn::VarBuilder;
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pub struct Lfm2GenerateModel<'a> {
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chat_template: ChatTemplate<'a>,
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tokenizer: TokenizerModel,
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device: Device,
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model: Lfm2Model,
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model_name: String,
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}
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impl<'a> Lfm2GenerateModel<'a> {
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pub fn init(path: &str, device: Option<&Device>, dtype: Option<DType>) -> Result<Self> {
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let chat_template = ChatTemplate::init(path)?;
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let tokenizer = TokenizerModel::init(path)?;
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let device = get_device(device);
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let gen_cfg_path = path.to_string() + "/generation_config.json";
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let gen_cfg: Lfm2GenerateConfig = serde_json::from_slice(&std::fs::read(gen_cfg_path)?)?;
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let cfg_path = path.to_string() + "/config.json";
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let cfg: Lfm2Config = serde_json::from_slice(&std::fs::read(cfg_path)?)?;
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let model_path = find_type_files(path, "safetensors")?;
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let cfg_dtype = if let Some(dtype) = &cfg.dtype {
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dtype.clone()
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} else if let Some(dtype) = &cfg.torch_dtype {
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dtype.clone()
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} else {
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"bfloat16".to_string()
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};
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let dtype = get_dtype(dtype, &cfg_dtype);
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let vb = unsafe { VarBuilder::from_mmaped_safetensors(&model_path, dtype, &device)? };
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let eos_ids = vec![gen_cfg.eos_token_id];
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let model = Lfm2Model::new(vb, &cfg, eos_ids)?;
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let model_name = std::path::Path::new(path)
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.file_name()
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.and_then(|s| s.to_str())
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.unwrap_or("lfm2")
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.to_string();
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Ok(Self {
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chat_template,
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tokenizer,
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device,
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model,
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model_name,
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})
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}
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}
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impl<'a> GenerationDataProvider for Lfm2GenerateModel<'a> {
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fn get_data(&self, mes: &crate::params::chat::ChatCompletionParameters) -> Result<PrepareData> {
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let mes_render = self.chat_template.apply_chat_template(mes)?;
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let in_reasoning = self.is_in_reasoning(&mes_render);
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let input_ids = self.tokenizer.text_encode(mes_render, &self.device)?;
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let multi_model_data = self.get_multi_model_data();
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Ok(PrepareData {
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in_reasoning,
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input_ids,
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multi_model_data,
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})
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}
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}
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crate::impl_generate_model!(Lfm2GenerateModel<'a>);
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