Move the llama2-c model in transformers. (#1205)

candle-examples/examples/llama2-c/main.rs

@@ -6,10 +6,10 @@ extern crate accelerate_src;
 #[cfg(feature = "mkl")]
 extern crate intel_mkl_src;
 
-mod model;
-mod qmodel;
+use candle_transformers::models::llama2_c as model;
+use candle_transformers::models::llama2_c_weights as weights;
+use candle_transformers::models::quantized_llama2_c as qmodel;
 mod training;
-mod weights;
 use clap::{Parser, Subcommand};
 
 use anyhow::{Error as E, Result};

candle-transformers/Cargo.toml

@@ -11,6 +11,7 @@ readme = "README.md"
 
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
+byteorder = { workspace = true }
 candle = { path = "../candle-core", version = "0.3.0", package = "candle-core" }
 candle-flash-attn = { path = "../candle-flash-attn", version = "0.3.0", optional = true }
 candle-nn = { path = "../candle-nn", version = "0.3.0" }

candle-transformers/src/models/llama2_c_weights.rs

@@ -1,9 +1,8 @@
-use anyhow::Result;
 use byteorder::{LittleEndian, ReadBytesExt};
-use candle::{DType, Device, IndexOp, Shape, Tensor};
+use candle::{DType, Device, IndexOp, Result, Shape, Tensor};
 use candle_nn::VarBuilder;
 
-use crate::model::Config;
+use super::llama2_c::Config;
 
 pub struct TransformerWeights {
     // token embedding table

candle-transformers/src/models/mod.rs

@@ -8,6 +8,8 @@ pub mod efficientnet;
 pub mod falcon;
 pub mod jina_bert;
 pub mod llama;
+pub mod llama2_c;
+pub mod llama2_c_weights;
 pub mod mistral;
 pub mod mixformer;
 pub mod mpt;
@@ -15,6 +17,7 @@ pub mod persimmon;
 pub mod quantized_blip;
 pub mod quantized_blip_text;
 pub mod quantized_llama;
+pub mod quantized_llama2_c;
 pub mod quantized_mistral;
 pub mod quantized_mixformer;
 pub mod quantized_mpt;

candle-transformers/src/models/quantized_llama2_c.rs

@@ -1,7 +1,7 @@
-use super::model::{Cache, Config};
+use super::llama2_c::{Cache, Config};
+use crate::quantized_nn::{linear_no_bias as linear, Embedding, Linear, RmsNorm};
+pub use crate::quantized_var_builder::VarBuilder;
 use candle::{DType, IndexOp, Module, Result, Tensor, D};
-use candle_transformers::quantized_nn::{linear_no_bias as linear, Embedding, Linear, RmsNorm};
-pub use candle_transformers::quantized_var_builder::VarBuilder;
 
 fn silu(xs: &Tensor) -> Result<Tensor> {
     xs / (xs.neg()?.exp()? + 1.0)?