model_card.rs

// SPDX-FileCopyrightText: Copyright (c) 2024-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
// SPDX-License-Identifier: Apache-2.0

//! # Model Deployment Card
//!
//! The ModelDeploymentCard (MDC) is the primary model configuration structure that will be available to any
//! component that needs to interact with the model or its dependent artifacts.
//!
//! The ModelDeploymentCard contains LLM model deployment configuration information:
//! - Display name and service name for the model
//! - Model information (ModelInfoType)
//! - Tokenizer configuration (TokenizerKind)
//! - Prompt formatter settings (PromptFormatterArtifact)

use std::fmt;
use std::fs::File;
use std::path::{Path, PathBuf};
use std::sync::Arc;

use crate::common::checked_file::CheckedFile;
use crate::local_model::runtime_config::ModelRuntimeConfig;
use crate::model_type::{ModelInput, ModelType};
use anyhow::{Context, Result};
use derive_builder::Builder;
use dynamo_runtime::DistributedRuntime;
use dynamo_runtime::storage::key_value_store::{
    EtcdStorage, Key, KeyValueStore, KeyValueStoreManager,
};
use dynamo_runtime::{slug::Slug, storage::key_value_store::Versioned, transports::nats};
use serde::{Deserialize, Serialize};
use tokenizers::Tokenizer as HfTokenizer;

use crate::gguf::{Content, ContentConfig, ModelConfigLike};
use crate::protocols::TokenIdType;

/// Identify model deployment cards in the key-value store
pub const ROOT_PATH: &str = "mdc";

#[derive(Serialize, Deserialize, Clone, Debug)]
#[serde(rename_all = "snake_case")]
pub enum ModelInfoType {
    HfConfigJson(CheckedFile),
    GGUF(PathBuf),
}

#[derive(Serialize, Deserialize, Clone, Debug)]
#[serde(rename_all = "snake_case")]
pub enum TokenizerKind {
    HfTokenizerJson(CheckedFile),
    GGUF(Box<HfTokenizer>),
}

/// Supported types of prompt formatters.
///
/// We need a way to associate the prompt formatter template definition with an associated
/// data model which is expected for rendering.
///
/// All current prompt formatters are Jinja2 templates which use the OpenAI ChatCompletionRequest
/// format. However, we currently do not have a discovery path to know if the model supports tool use
/// unless we inspect the template.
///
/// TODO(): Add an enum for the PromptFormatDataModel with at minimum arms for:
/// - OaiChat
/// - OaiChatToolUse
#[derive(Serialize, Deserialize, Clone, Debug)]
#[serde(rename_all = "snake_case")]
pub enum PromptFormatterArtifact {
    HfTokenizerConfigJson(CheckedFile),
    HfChatTemplate(CheckedFile),
    GGUF(PathBuf),
}

#[derive(Serialize, Deserialize, Clone, Debug, PartialEq, Eq, Hash)]
#[serde(rename_all = "snake_case")]
pub enum PromptContextMixin {
    /// Support OAI Chat Messages and Tools
    OaiChat,

    /// Enables templates with `{{datetime}}` to be rendered with the current date and time.
    Llama3DateTime,
}

#[derive(Serialize, Deserialize, Clone, Debug)]
#[serde(rename_all = "snake_case")]
pub enum GenerationConfig {
    HfGenerationConfigJson(CheckedFile),
    GGUF(PathBuf),
}

#[derive(Serialize, Deserialize, Clone, Debug, Builder, Default)]
pub struct ModelDeploymentCard {
    /// Human readable model name, e.g. "Meta Llama 3.1 8B Instruct"
    pub display_name: String,

    // Cache the Slugified display_name so we can share references to it
    slug: Slug,

    /// Model information
    pub model_info: Option<ModelInfoType>,

    /// Tokenizer configuration
    pub tokenizer: Option<TokenizerKind>,

    /// Prompt Formatter configuration
    #[serde(default, skip_serializing_if = "Option::is_none")]
    pub prompt_formatter: Option<PromptFormatterArtifact>,

    /// chat template may be stored as a separate file instead of in `prompt_formatter`.
    #[serde(default, skip_serializing_if = "Option::is_none")]
    pub chat_template_file: Option<PromptFormatterArtifact>,

    /// Generation config - default sampling params
    #[serde(default, skip_serializing_if = "Option::is_none")]
    pub gen_config: Option<GenerationConfig>,

    /// Prompt Formatter Config
    #[serde(default, skip_serializing_if = "Option::is_none")]
    pub prompt_context: Option<Vec<PromptContextMixin>>,

    /// Max context (in number of tokens) this model can handle
    pub context_length: u32,

    /// Size of a KV cache block - vllm only currently
    /// Passed to the engine and the KV router.
    pub kv_cache_block_size: u32,

    /// How many times a request can be migrated to another worker if the HTTP server lost
    /// connection to the current worker.
    pub migration_limit: u32,

    /// Specifies whether the model is a chat, completions, etc model.
    pub model_type: ModelType,

    /// Specifies the model input type.
    /// `Tokens` for engines that expect pre-processed input.
    /// `Text` for engines that take care of pre-processing themselves.
    pub model_input: ModelInput,

    /// User-defined metadata for custom worker behavior
    #[serde(default, skip_serializing_if = "Option::is_none")]
    pub user_data: Option<serde_json::Value>,

    #[serde(default)]
    pub runtime_config: ModelRuntimeConfig,

    #[serde(skip)]
    cache_dir: Option<Arc<tempfile::TempDir>>,
}

impl ModelDeploymentCard {
    pub fn builder() -> ModelDeploymentCardBuilder {
        ModelDeploymentCardBuilder::default()
    }

    /// Create a ModelDeploymentCard where only the name is filled in.
    ///
    /// Single-process setups don't need an MDC to communicate model details, but it
    /// simplifies the code to assume we always have one. This is how you get one in those
    /// cases. A quasi-null object: <https://en.wikipedia.org/wiki/Null_object_pattern>
    pub fn with_name_only(name: &str) -> ModelDeploymentCard {
        ModelDeploymentCard {
            display_name: name.to_string(),
            slug: Slug::from_string(name),
            ..Default::default()
        }
    }

    /// Load a model deployment card from a JSON file
    pub fn load_from_json_file<P: AsRef<Path>>(file: P) -> std::io::Result<Self> {
        let contents = std::fs::read_to_string(&file)?;
        Ok(serde_json::from_str(&contents).inspect_err(|err| {
            crate::log_json_err(&file.as_ref().display().to_string(), &contents, err)
        })?)
    }

    /// Load a model deployment card from a JSON string
    pub fn load_from_json_str(contents: &str) -> Result<Self, anyhow::Error> {
        Ok(serde_json::from_str(contents)
            .inspect_err(|err| crate::log_json_err("unknown", contents, err))?)
    }

    //
    // Methods
    //

    /// Save the model deployment card to a JSON file
    pub fn save_to_json_file(&self, file: &str) -> Result<(), anyhow::Error> {
        std::fs::write(file, self.to_json()?)?;
        Ok(())
    }

    pub fn slug(&self) -> &Slug {
        &self.slug
    }

    /// Serialize the model deployment card to a JSON string
    pub fn to_json(&self) -> Result<String, anyhow::Error> {
        Ok(serde_json::to_string(self)?)
    }

    pub fn mdcsum(&self) -> String {
        let json = self.to_json().unwrap();
        format!("{}", blake3::hash(json.as_bytes()))
    }

    /// Is this a full model card with tokenizer?
    /// There are cases where we have a placeholder card (see `with_name_only`).
    pub fn has_tokenizer(&self) -> bool {
        self.tokenizer.is_some()
    }

    pub fn tokenizer_hf(&self) -> anyhow::Result<HfTokenizer> {
        match &self.tokenizer {
            Some(TokenizerKind::HfTokenizerJson(checked_file)) => {
                let p = checked_file.path().ok_or_else(|| {
                    anyhow::anyhow!("Tokenizer is URL-backed ({:?})", checked_file.url())
                })?;
                HfTokenizer::from_file(p)
                    .inspect_err(|err| {
                        if let Some(serde_err) = err.downcast_ref::<serde_json::Error>()
                            && let Ok(contents) = std::fs::read_to_string(p)
                        {
                            crate::log_json_err(&p.display().to_string(), &contents, serde_err);
                        }
                    })
                    .map_err(anyhow::Error::msg)
                    .with_context(|| p.display().to_string())
            }
            Some(TokenizerKind::GGUF(t)) => Ok(*t.clone()),
            None => {
                anyhow::bail!("Blank ModelDeploymentCard does not have a tokenizer");
            }
        }
    }

    pub fn is_gguf(&self) -> bool {
        match &self.model_info {
            Some(info) => info.is_gguf(),
            None => false,
        }
    }

    /// Move the files this MDC uses into the NATS object store.
    /// Updates the URI's to point to NATS.
    pub async fn move_to_nats(&mut self, nats_client: nats::Client) -> Result<()> {
        let nats_addr = nats_client.addr();
        let bucket_name = self.slug().clone();
        tracing::debug!(
            nats_addr,
            %bucket_name,
            "Uploading model deployment card fields to NATS"
        );

        macro_rules! nats_upload {
            ($field:expr, $enum_variant:path, $filename:literal) => {
                if let Some($enum_variant(src_file)) = $field.as_mut()
                    && let Some(path) = src_file.path()
                {
                    let target = format!("nats://{nats_addr}/{bucket_name}/{}", $filename);
                    let dest = url::Url::parse(&target)?;
                    nats_client.object_store_upload(path, &dest).await?;
                    src_file.move_to_url(dest);
                }
            };
        }

        nats_upload!(self.model_info, ModelInfoType::HfConfigJson, "config.json");
        nats_upload!(
            self.gen_config,
            GenerationConfig::HfGenerationConfigJson,
            "generation_config.json"
        );
        nats_upload!(
            self.prompt_formatter,
            PromptFormatterArtifact::HfTokenizerConfigJson,
            "tokenizer_config.json"
        );
        nats_upload!(
            self.chat_template_file,
            PromptFormatterArtifact::HfChatTemplate,
            "chat_template.jinja"
        );
        nats_upload!(
            self.tokenizer,
            TokenizerKind::HfTokenizerJson,
            "tokenizer.json"
        );

        Ok(())
    }

    /// Move the files this MDC uses from the NATS object store to local disk.
    /// Updates the URI's to point to the created files.
    ///
    /// The returned TempDir must be kept alive, it cleans up on drop.
    async fn move_from_nats(&mut self, nats_client: nats::Client) -> Result<tempfile::TempDir> {
        let nats_addr = nats_client.addr();
        let bucket_name = self.slug();
        let target_dir = tempfile::TempDir::with_prefix(bucket_name.to_string())?;
        tracing::debug!(
            nats_addr,
            %bucket_name,
            target_dir = %target_dir.path().display(),
            "Downloading model deployment card fields from NATS"
        );

        macro_rules! nats_download {
            ($field:expr, $enum_variant:path, $filename:literal) => {
                if let Some($enum_variant(src_file)) = $field.as_mut()
                    && let Some(src_url) = src_file.url()
                {
                    let target = target_dir.path().join($filename);
                    nats_client.object_store_download(src_url, &target).await?;
                    if !src_file.checksum_matches(&target) {
                        anyhow::bail!(
                            "Invalid {} in NATS for {}, checksum does not match.",
                            $filename,
                            self.display_name
                        );
                    }
                    src_file.move_to_disk(target);
                }
            };
        }

        nats_download!(self.model_info, ModelInfoType::HfConfigJson, "config.json");
        nats_download!(
            self.gen_config,
            GenerationConfig::HfGenerationConfigJson,
            "generation_config.json"
        );
        nats_download!(
            self.prompt_formatter,
            PromptFormatterArtifact::HfTokenizerConfigJson,
            "tokenizer_config.json"
        );
        nats_download!(
            self.chat_template_file,
            PromptFormatterArtifact::HfChatTemplate,
            "chat_template.jinja"
        );
        nats_download!(
            self.tokenizer,
            TokenizerKind::HfTokenizerJson,
            "tokenizer.json"
        );

        Ok(target_dir)
    }

    /// Delete this card from the key-value store and it's URLs from the object store
    pub async fn delete_from_nats(&mut self, nats_client: nats::Client) -> Result<()> {
        let nats_addr = nats_client.addr();
        let bucket_name = self.slug();
        tracing::trace!(
            nats_addr,
            %bucket_name,
            "Delete model deployment card from NATS"
        );
        nats_client
            .object_store_delete_bucket(bucket_name.as_ref())
            .await
    }

    /// Allow user to override the name we register this model under.
    /// Corresponds to vllm's `--served-model-name`.
    pub fn set_name(&mut self, name: &str) {
        self.display_name = name.to_string();
        self.slug = Slug::from_string(name);
    }

    /// Build an in-memory ModelDeploymentCard from either:
    /// - a folder containing config.json, tokenizer.json and token_config.json
    /// - a GGUF file
    ///   With an optional custom template
    pub fn load_from_disk(
        config_path: impl AsRef<Path>,
        custom_template_path: Option<&Path>,
    ) -> anyhow::Result<ModelDeploymentCard> {
        let config_path = config_path.as_ref();
        if config_path.is_dir() {
            Self::from_local_path(config_path, custom_template_path)
        } else {
            // GGUF files don't support custom templates yet
            if custom_template_path.is_some() {
                anyhow::bail!("Custom templates are not supported for GGUF files");
            }
            Self::from_gguf(config_path)
        }
    }

    pub fn requires_preprocessing(&self) -> bool {
        matches!(self.model_input, ModelInput::Tokens)
    }

    /// Load a ModelDeploymentCard from storage the DistributedRuntime is configured to use.
    /// Card should be fully local and ready to use when the call returns.
    pub async fn load_from_store(
        mdc_key: &Key,
        drt: &DistributedRuntime,
    ) -> anyhow::Result<Option<Self>> {
        let Some(etcd_client) = drt.etcd_client() else {
            // Should be impossible because we only get here on an etcd event
            anyhow::bail!("Missing etcd_client");
        };
        let store: Box<dyn KeyValueStore> = Box::new(EtcdStorage::new(etcd_client));
        let card_store = Arc::new(KeyValueStoreManager::new(store));
        let Some(mut card) = card_store
            .load::<ModelDeploymentCard>(ROOT_PATH, mdc_key)
            .await?
        else {
            return Ok(None);
        };
        // This cache_dir is a tempfile::TempDir will be deleted on drop, so keep it alive.
        card.cache_dir = Some(Arc::new(card.move_from_nats(drt.nats_client()).await?));
        Ok(Some(card))
    }

    /// Creates a ModelDeploymentCard from a local directory path.
    ///
    /// Currently HuggingFace format is supported and following files are expected:
    /// - config.json: Model configuration in HuggingFace format
    /// - tokenizer.json: Tokenizer configuration in HuggingFace format
    /// - tokenizer_config.json: Optional prompt formatter configuration
    ///
    /// # Arguments
    /// * `local_root_dir` - Path to the local model directory
    ///
    /// # Errors
    /// Returns an error if:
    /// - The path doesn't exist or isn't a directory
    /// - The path contains invalid Unicode characters
    /// - Required model files are missing or invalid
    fn from_local_path(
        local_root_dir: impl AsRef<Path>,
        custom_template_path: Option<&Path>,
    ) -> anyhow::Result<Self> {
        let local_root_dir = local_root_dir.as_ref();
        check_valid_local_repo_path(local_root_dir)?;
        let repo_id = local_root_dir
            .canonicalize()?
            .to_str()
            .ok_or_else(|| anyhow::anyhow!("Path contains invalid Unicode"))?
            .to_string();
        let model_name = local_root_dir
            .file_name()
            .and_then(|n| n.to_str())
            .ok_or_else(|| anyhow::anyhow!("Invalid model directory name"))?;

        Self::from_repo(&repo_id, model_name, custom_template_path)
    }

    fn from_gguf(gguf_file: &Path) -> anyhow::Result<Self> {
        let model_name = gguf_file
            .iter()
            .next_back()
            .map(|n| n.to_string_lossy().to_string());
        let Some(model_name) = model_name else {
            // I think this would only happy on an empty path
            anyhow::bail!(
                "Could not extract model name from path '{}'",
                gguf_file.display()
            );
        };

        // TODO: we do this in HFConfig also, unify
        let content = load_gguf(gguf_file)?;
        let context_length = content.get_metadata()[&format!("{}.context_length", content.arch())]
            .to_u32()
            .unwrap_or(0);
        tracing::debug!(context_length, "Loaded context length from GGUF");

        Ok(Self {
            display_name: model_name.to_string(),
            slug: Slug::from_string(model_name),
            model_info: Some(ModelInfoType::GGUF(gguf_file.to_path_buf())),
            tokenizer: Some(TokenizerKind::from_gguf(gguf_file)?),
            gen_config: None, // AFAICT there is no equivalent in a GGUF
            prompt_formatter: Some(PromptFormatterArtifact::GGUF(gguf_file.to_path_buf())),
            chat_template_file: None,
            prompt_context: None, // TODO - auto-detect prompt context
            context_length,
            kv_cache_block_size: 0,
            migration_limit: 0,
            model_type: Default::default(),  // set later
            model_input: Default::default(), // set later
            user_data: None,
            runtime_config: ModelRuntimeConfig::default(),
            cache_dir: None,
        })
    }

    fn from_repo(
        repo_id: &str,
        model_name: &str,
        custom_template_path: Option<&Path>,
    ) -> anyhow::Result<Self> {
        // This is usually the right choice
        let context_length = crate::file_json_field(
            &PathBuf::from(repo_id).join("config.json"),
            "max_position_embeddings",
        )
        // But sometimes this is
        .or_else(|_| {
            crate::file_json_field(
                &PathBuf::from(repo_id).join("tokenizer_config.json"),
                "model_max_length",
            )
        })
        // If neither of those are present let the engine default it
        .unwrap_or(0);

        // Load chat template - either custom or from repo
        let chat_template_file = if let Some(template_path) = custom_template_path {
            if !template_path.exists() {
                anyhow::bail!(
                    "Custom template file does not exist: {}",
                    template_path.display()
                );
            }

            // Verify the file is readable
            let _template_content = std::fs::read_to_string(template_path).with_context(|| {
                format!(
                    "Failed to read custom template file: {}",
                    template_path.display()
                )
            })?;

            Some(PromptFormatterArtifact::HfChatTemplate(
                CheckedFile::from_disk(template_path)?,
            ))
        } else {
            PromptFormatterArtifact::chat_template_from_repo(repo_id)?
        };

        Ok(Self {
            display_name: model_name.to_string(),
            slug: Slug::from_string(model_name),
            model_info: Some(ModelInfoType::from_repo(repo_id)?),
            tokenizer: Some(TokenizerKind::from_repo(repo_id)?),
            gen_config: GenerationConfig::from_repo(repo_id).ok(), // optional
            prompt_formatter: PromptFormatterArtifact::from_repo(repo_id)?,
            chat_template_file,
            prompt_context: None, // TODO - auto-detect prompt context
            context_length,
            kv_cache_block_size: 0, // set later
            migration_limit: 0,
            model_type: Default::default(),  // set later
            model_input: Default::default(), // set later
            user_data: None,
            runtime_config: ModelRuntimeConfig::default(),
            cache_dir: None,
        })
    }
}

impl PartialEq for ModelDeploymentCard {
    fn eq(&self, other: &ModelDeploymentCard) -> bool {
        self.mdcsum() == other.mdcsum()
    }
}

/// A ModelDeploymentCard is published a single time per instance and never updated.
impl Versioned for ModelDeploymentCard {
    fn revision(&self) -> u64 {
        0
    }

    fn set_revision(&mut self, _revision: u64) {}
}

impl fmt::Display for ModelDeploymentCard {
    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
        write!(f, "{}", self.slug())
    }
}
pub trait ModelInfo: Send + Sync {
    /// Model type
    fn model_type(&self) -> String;

    /// Token ID for the beginning of sequence
    fn bos_token_id(&self) -> TokenIdType;

    /// Token ID for the end of sequence
    fn eos_token_ids(&self) -> Vec<TokenIdType>;

    /// Maximum position embeddings / max sequence length
    /// TODO: This is only used in a single test, no other code. Remove?
    fn max_position_embeddings(&self) -> Option<usize>;

    /// Vocabulary size
    /// TODO: This is only used in a single test, no other code. Remove?
    fn vocab_size(&self) -> Option<usize>;
}

impl ModelInfoType {
    pub fn get_model_info(&self) -> Result<Arc<dyn ModelInfo>> {
        match self {
            Self::HfConfigJson(checked_file) => {
                let Some(path) = checked_file.path() else {
                    anyhow::bail!("model info is not a local path: {checked_file:?}");
                };
                Ok(HFConfig::from_json_file(path)?)
            }
            Self::GGUF(path) => Ok(HFConfig::from_gguf(path)?),
        }
    }
    pub fn is_gguf(&self) -> bool {
        matches!(self, Self::GGUF(_))
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
struct HFConfig {
    /// denotes the mixin to the flattened data model which can be present
    /// in the config.json file
    architectures: Vec<String>,

    /// general model type
    model_type: String,

    text_config: Option<HFTextConfig>,

    // Sometimes it's inside HFTextConfig, sometimes it's here
    eos_token_id: Option<serde_json::Value>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
struct HFTextConfig {
    // It can take multiple attempts to load this, so Option
    bos_token_id: Option<TokenIdType>,

    // We set this once bos_token_id is loaded so we don't have to deal with Option
    #[serde(default)]
    final_bos_token_id: TokenIdType,

    eos_token_id: Option<serde_json::Value>,

    #[serde(default)]
    final_eos_token_ids: Vec<TokenIdType>,

    /// max sequence length
    max_position_embeddings: Option<usize>,

    /// number of layers in the model
    num_hidden_layers: usize,

    /// number of attention heads in the model
    num_attention_heads: Option<usize>,

    /// Vocabulary size
    vocab_size: Option<usize>,
}

impl HFConfig {
    fn from_json_file<P: AsRef<Path>>(file: P) -> Result<Arc<dyn ModelInfo>> {
        let file_path = file.as_ref();
        let contents = std::fs::read_to_string(file_path)?;
        let mut config: Self = json_five::from_str(&contents)
            .inspect_err(|err| {
                tracing::error!(path=%file_path.display(), %err, "Failed to parse config.json as JSON5");
            })?;
        if config.text_config.is_none() {
            let text_config: HFTextConfig = json_five::from_str(&contents)
                .inspect_err(|err| {
                    tracing::error!(path=%file_path.display(), %err, "Failed to parse text config from config.json as JSON5");
                })?;
            config.text_config = Some(text_config);
        }

        // Sometimes bos_token_id is in generation_config.json not config.json
        let Some(text_config) = config.text_config.as_mut() else {
            anyhow::bail!(
                "Missing text config fields (model_type, eos_token_ids, etc) in config.json"
            );
        };

        let gencfg_path = file_path
            .parent()
            .unwrap_or_else(|| Path::new(""))
            .join("generation_config.json");
        if text_config.bos_token_id.is_none() {
            let bos_token_id = crate::file_json_field::<TokenIdType>(&gencfg_path, "bos_token_id")
                .context(
                    "missing bos_token_id in generation_config.json and config.json, cannot load",
                )?;
            text_config.bos_token_id = Some(bos_token_id);
        }
        // Now that we have it for sure, set it in the non-Option field
        let final_bos_token_id = text_config.bos_token_id.take().unwrap();
        text_config.final_bos_token_id = final_bos_token_id;

        // TODO: refactor this when we switch to per-architecture tokenization
        let final_eos_token_ids: Vec<TokenIdType> = config
            .eos_token_id
            .as_ref()
            .or(text_config.eos_token_id.as_ref())
            .and_then(|v| {
                if v.is_number() {
                    v.as_number()
                        .and_then(|n| n.as_u64())
                        .map(|n| vec![n as TokenIdType])
                } else if v.is_array() {
                    let arr = v.as_array().unwrap(); // Safety: We just checked
                    Some(
                        arr.iter()
                            .filter_map(|inner_v| {
                                inner_v
                                    .as_number()
                                    .and_then(|n| n.as_u64())
                                    .map(|n| n as TokenIdType)
                            })
                            .collect(),
                    )
                } else {
                    tracing::error!(
                        ?v,
                        path = %file_path.display(),
                        "eos_token_id is not a number or an array, cannot use"
                    );
                    None
                }
            })
            .or_else(|| {
                // Maybe it's in generation_config.json
                crate::file_json_field(&gencfg_path, "eos_token_id")
                .inspect_err(
                    |err| tracing::warn!(%err, "Missing eos_token_id in generation_config.json"),
                )
                .ok()
            })
            .ok_or_else(|| {
                anyhow::anyhow!(
                    "missing eos_token_id in config.json and generation_config.json, cannot load"
                )
            })?;
        text_config.final_eos_token_ids = final_eos_token_ids;

        Ok(Arc::new(config))
    }
    fn from_gguf(gguf_file: &Path) -> Result<Arc<dyn ModelInfo>> {
        let content = load_gguf(gguf_file)?;
        let model_config_metadata: ContentConfig = (&content).into();
        let num_hidden_layers =
            content.get_metadata()[&format!("{}.block_count", content.arch())].to_u32()? as usize;

        let bos_token_id = content.get_metadata()["tokenizer.ggml.bos_token_id"].to_u32()?;
        let eos_token_id = content.get_metadata()["tokenizer.ggml.eos_token_id"].to_u32()?;

        // to_vec returns a Vec that's already there, so it's cheap
        let vocab_size = content.get_metadata()["tokenizer.ggml.tokens"]
            .to_vec()?
            .len();

        let arch = content.arch().to_string();
        Ok(Arc::new(HFConfig {
            architectures: vec![format!("{}ForCausalLM", capitalize(&arch))],
            // "general.architecture"
            model_type: arch,
            text_config: Some(HFTextConfig {
                bos_token_id: None,
                final_bos_token_id: bos_token_id,

                eos_token_id: None,
                final_eos_token_ids: vec![eos_token_id],

                // "llama.context_length"
                max_position_embeddings: Some(model_config_metadata.max_seq_len()),
                // "llama.block_count"
                num_hidden_layers,
                // "llama.attention.head_count"
                num_attention_heads: Some(model_config_metadata.num_attn_heads()),
                // "tokenizer.ggml.tokens".len()
                vocab_size: Some(vocab_size),
            }),
            eos_token_id: None,
        }))
    }
}

impl ModelInfo for HFConfig {
    fn model_type(&self) -> String {
        self.model_type.clone()
    }

    fn bos_token_id(&self) -> TokenIdType {
        self.text_config.as_ref().unwrap().final_bos_token_id
    }

    fn eos_token_ids(&self) -> Vec<TokenIdType> {
        self.text_config
            .as_ref()
            .unwrap()
            .final_eos_token_ids
            .clone()
    }

    fn max_position_embeddings(&self) -> Option<usize> {
        self.text_config.as_ref().unwrap().max_position_embeddings
    }

    fn vocab_size(&self) -> Option<usize> {
        self.text_config.as_ref().unwrap().vocab_size
    }
}

impl TokenizerKind {
    pub fn from_gguf(gguf_file: &Path) -> anyhow::Result<Self> {
        let content = load_gguf(gguf_file)?;
        let out = crate::gguf::convert_gguf_to_hf_tokenizer(&content)
            .with_context(|| gguf_file.display().to_string())?;
        Ok(TokenizerKind::GGUF(Box::new(out.tokenizer)))
    }
}

pub(crate) fn load_gguf(gguf_file: &Path) -> anyhow::Result<Content> {
    let filename = gguf_file.display().to_string();
    let mut f = File::open(gguf_file).with_context(|| filename.clone())?;
    // vec because GGUF can be split into multiple files (shards)
    let mut readers = vec![&mut f];
    crate::gguf::Content::from_readers(&mut readers).with_context(|| filename.clone())
}

fn capitalize(s: &str) -> String {
    let mut chars = s.chars();
    match chars.next() {
        None => String::new(),
        Some(first) => first.to_uppercase().collect::<String>() + &chars.as_str().to_lowercase(),
    }
}

impl ModelInfoType {
    pub fn from_repo(repo_id: &str) -> Result<Self> {
        let f = CheckedFile::from_disk(PathBuf::from(repo_id).join("config.json"))
            .with_context(|| format!("unable to extract config.json from repo {repo_id}"))?;
        Ok(Self::HfConfigJson(f))
    }
}

impl GenerationConfig {
    pub fn from_repo(repo_id: &str) -> Result<Self> {
        let f = CheckedFile::from_disk(PathBuf::from(repo_id).join("generation_config.json"))
            .with_context(|| format!("unable to extract generation_config from repo {repo_id}"))?;
        Ok(Self::HfGenerationConfigJson(f))
    }
}

impl PromptFormatterArtifact {
    pub fn from_repo(repo_id: &str) -> Result<Option<Self>> {
        // we should only error if we expect a prompt formatter and it's not found
        // right now, we don't know when to expect it, so we just return Ok(Some/None)
        match CheckedFile::from_disk(PathBuf::from(repo_id).join("tokenizer_config.json")) {
            Ok(f) => Ok(Some(Self::HfTokenizerConfigJson(f))),
            Err(_) => Ok(None),
        }
    }

    pub fn chat_template_from_repo(repo_id: &str) -> Result<Option<Self>> {
        match CheckedFile::from_disk(PathBuf::from(repo_id).join("chat_template.jinja")) {
            Ok(f) => Ok(Some(Self::HfChatTemplate(f))),
            Err(_) => Ok(None),
        }
    }
}

impl TokenizerKind {
    pub fn from_repo(repo_id: &str) -> Result<Self> {
        let f = CheckedFile::from_disk(PathBuf::from(repo_id).join("tokenizer.json"))
            .with_context(|| format!("unable to extract tokenizer kind from repo {repo_id}"))?;
        Ok(Self::HfTokenizerJson(f))
    }
}

/// Checks if the provided path is a valid local repository path.
///
/// # Arguments
/// * `path` - Path to validate
///
/// # Errors
/// Returns an error if the path doesn't exist or isn't a directory
fn check_valid_local_repo_path(path: impl AsRef<Path>) -> Result<()> {
    let path = path.as_ref();
    if !path.exists() {
        return Err(anyhow::anyhow!(
            "Model path does not exist: {}",
            path.display()
        ));
    }

    if !path.is_dir() {
        return Err(anyhow::anyhow!(
            "Model path is not a directory: {}",
            path.display()
        ));
    }
    Ok(())
}

#[cfg(test)]
mod tests {
    use super::HFConfig;
    use std::path::Path;

    #[test]
    pub fn test_config_json_llama3() -> anyhow::Result<()> {
        let config_file = Path::new(env!("CARGO_MANIFEST_DIR"))
            .join("tests/data/sample-models/mock-llama-3.1-8b-instruct/config.json");
        let config = HFConfig::from_json_file(&config_file)?;
        assert_eq!(config.bos_token_id(), 128000);
        Ok(())
    }

    #[test]
    pub fn test_config_json_llama4() -> anyhow::Result<()> {
        let config_file = Path::new(env!("CARGO_MANIFEST_DIR"))
            .join("tests/data/sample-models/Llama-4-Scout-17B-16E-Instruct/config.json");
        let config = HFConfig::from_json_file(&config_file)?;
        assert_eq!(config.bos_token_id(), 200000);
        Ok(())
    }

    /// The Python JSON parser accepts `Infinity` as a numeric value. This is explicitly against the
    /// JSON spec, but inevitably people rely on it, so we have to allow it.
    /// We treat that file as JSON5 (a lenient superset of JSON) to be able to parse it.
    #[test]
    fn test_invalid_json_but_py_accepts_it() {
        dynamo_runtime::logging::init();
        let path = "tests/data/sample-models/NVIDIA-Nemotron-Nano-12B-v2-Base/config.json";
        let _ = HFConfig::from_json_file(path).unwrap();
    }
}