Adds support for stella_en_v5 embedding model -400M variant

candle-examples/examples/stella-en-v5/README.md

@@ -21,7 +21,7 @@ Stella_en_1.5B_v5 is trained by [MRL](https://arxiv.org/abs/2205.13147) enabling
 The following reproduces the example in the [model card](https://huggingface.co/dunzhang/stella_en_1.5B_v5) for a retrieval task (s2p). The sample queries and docs are hardcoded in the example.
 
 ```bash
-$ cargo run --example stella-en-v5 --release --features <metal | cuda>
+$ cargo run --example stella-en-v5 --release --features <metal | cuda> -- --which 1.5b
 
 >
 > Score: 0.8178786
@@ -37,9 +37,29 @@ $ cargo run --example stella-en-v5 --release --features <metal | cuda>
 > caused by free radicals. Regular consumption of green tea has been associated with improved heart health, enhanced cognitive function, and a reduced risk of certain types >
 > of cancer. The polyphenols in green tea may also have anti-inflammatory and weight loss properties.
 >
+
+$ cargo run --example stella-en-v5 --release --features <metal | cuda> -- --which 400m
+
+>
+> Score: 0.8397539
+> Query: What are some ways to reduce stress?
+> Answer: There are many effective ways to reduce stress. Some common techniques include deep breathing, meditation, and physical activity. Engaging in hobbies, spending
+> time in nature, and connecting with loved ones can also help alleviate stress. Additionally, setting boundaries, practicing self-care, and learning to say no can prevent
+> stress from building up.
+>
+>
+>
+> Score: 0.809545
+> Query: What are the benefits of drinking green tea?
+> Answer: Green tea has been consumed for centuries and is known for its potential health benefits. It contains antioxidants that may help protect the body against damage
+> caused by free radicals. Regular consumption of green tea has been associated with improved heart health, enhanced cognitive function, and a reduced risk of certain types
+> of cancer. The polyphenols in green tea may also have anti-inflammatory and weight loss properties.
+>
 ```
 
 ## Supported options:
-- `Stella_en_15B_v5` supports 256, 768, 1024, 2048, 4096, 6144 and 8192 embedding dimensions (though the model card mentions 512, I couldn't find weights for the same). In the example run this is supported with `--embed-dim` option. E.g. `... --embed-dim 4096`. Defaults to `1024`.
+- `Stella_en_v5` has 2 model variants published - a 1.5B variant and 400M variant. This is enabled through the flag `--which`. E.g. `--which 400m` or `--which 1.5b`.
+
+- `Stella_en_v5` supports 256, 768, 1024, 2048, 4096, 6144 and 8192 embedding dimensions (though the model card mentions 512, I couldn't find weights for the same). In the example run this is supported with `--embed-dim` option. E.g. `... --embed-dim 4096`. Defaults to `1024`.
 
 - As per the [model card](https://huggingface.co/dunzhang/stella_en_1.5B_v5), the model has been primarily trained on `s2s` (similarity) and `s2p` (retrieval) tasks. These require a slightly different `query` preprocessing (a different prompt template for each). In this example this is enabled though `--task` option.

candle-examples/examples/stella-en-v5/main.rs

@@ -212,13 +212,24 @@ impl EncodeTask {
     }
 }
 
+#[derive(Clone, Debug, Copy, PartialEq, Eq, clap::ValueEnum)]
+enum Which {
+    #[value(name = "1.5b")]
+    Large,
+    #[value(name = "400m")]
+    Small,
+}
+
 #[derive(Parser, Debug)]
 #[command(author, version, about, long_about = None)]
 struct Args {
     /// Run on CPU rather than on GPU.
     #[arg(long)]
     cpu: bool,
 
+    #[arg(long)]
+    which: Which,
+
     /// Enable tracing (generates a trace-timestamp.json file).
     #[arg(long)]
     tracing: bool,
@@ -250,24 +261,33 @@ struct Args {
 
 // Tokenizer creation is super critical in our case.
 // We are going to be `padding: Left` for each batch
-fn create_tokenizer(tokenizer_file: &Path) -> Result<Tokenizer> {
+fn create_tokenizer(tokenizer_file: &Path, which: Which) -> Result<Tokenizer> {
     let mut tokenizer = Tokenizer::from_file(tokenizer_file).map_err(E::msg)?;
-    let pad_id = if let Some(pad_id) = tokenizer.token_to_id("<|endoftext|>") {
-        pad_id
-    } else {
-        return Err(anyhow!(
-            "Tokenizer doesn't contain expected `<|endoftext|>` token"
-        ));
-    };
 
-    // This part is super important, we are padding the tokens to the *`left`* and not the usual *`right`* padding
-    tokenizer.with_padding(Some(PaddingParams {
-        strategy: PaddingStrategy::BatchLongest,
-        direction: PaddingDirection::Left,
-        pad_id,
-        pad_token: "<|endoftext|>".to_string(),
-        ..Default::default()
-    }));
+    if which == Which::Large {
+        let pad_id = if let Some(pad_id) = tokenizer.token_to_id("<|endoftext|>") {
+            pad_id
+        } else {
+            return Err(anyhow!(
+                "Tokenizer doesn't contain expected `<|endoftext|>` token"
+            ));
+        };
+
+        // This part is super important, we are padding the tokens to the *`left`* and not the usual *`right`* padding
+        tokenizer.with_padding(Some(PaddingParams {
+            strategy: PaddingStrategy::BatchLongest,
+            direction: PaddingDirection::Left,
+            pad_id,
+            pad_token: "<|endoftext|>".to_string(),
+            ..Default::default()
+        }));
+    } else {
+        tokenizer.with_padding(Some(PaddingParams {
+            strategy: PaddingStrategy::BatchLongest,
+            direction: PaddingDirection::Right,
+            ..Default::default()
+        }));
+    }
 
     Ok(tokenizer)
 }
@@ -298,7 +318,19 @@ fn main() -> Result<()> {
         Some(d) => d,
         None => EmbedDim::Dim1024,
     };
-    let repo = api.repo(Repo::model("dunzhang/stella_en_1.5B_v5".to_string()));
+
+    let (repo, cfg) = match args.which {
+        Which::Large => (
+            "dunzhang/stella_en_1.5B_v5",
+            Config::new_1_5_b_v5(embed_dim.embed_dim()),
+        ),
+        Which::Small => (
+            "dunzhang/stella_en_400M_v5",
+            Config::new_400_m_v5(embed_dim.embed_dim()),
+        ),
+    };
+
+    let repo = api.repo(Repo::model(repo.to_string()));
     let tokenizer_filename = match args.tokenizer_file {
         Some(file) => std::path::PathBuf::from(file),
         None => repo.get("tokenizer.json")?,
@@ -330,7 +362,7 @@ fn main() -> Result<()> {
     println!("retrieved the files in {:?}", start.elapsed());
 
     // Initializing the tokenizer which would require us to add padding to the `left` for batch encoding
-    let tokenizer = create_tokenizer(tokenizer_filename.as_path())?;
+    let tokenizer = create_tokenizer(tokenizer_filename.as_path(), args.which)?;
 
     let start = std::time::Instant::now();
 
@@ -343,11 +375,7 @@ fn main() -> Result<()> {
     let embed_vb =
         unsafe { VarBuilder::from_mmaped_safetensors(&embed_weight_files, DType::F32, &device)? };
 
-    let model = EmbeddingModel::new(
-        &Config::new_1_5_b_v5(embed_dim.embed_dim()),
-        base_vb,
-        embed_vb,
-    )?;
+    let model = EmbeddingModel::new(&cfg, base_vb, embed_vb)?;
 
     println!("loaded the model in {:?}", start.elapsed());