helios-engine 0.5.5

//! # Candle Models Module
//!
//! This module provides implementations for running various model architectures using Candle.
//! It includes support for Qwen, Llama, Gemma, Mistral and other model types.

use crate::error::{HeliosError, Result};
use std::path::Path;

#[cfg(feature = "candle")]
use candle_core::Device;

/// Trait for model inference implementations
#[cfg(feature = "candle")]
pub trait ModelInference: Send + Sync {
    /// Generate text given a prompt
    fn generate(&mut self, prompt: &str, max_tokens: usize) -> Result<String>;

    /// Get the model's max sequence length
    fn max_seq_len(&self) -> usize;
}

/// Qwen model wrapper for text generation
#[cfg(feature = "candle")]
pub struct QwenModel {
    #[allow(dead_code)]
    model: Box<dyn std::any::Any>,
    #[allow(dead_code)]
    device: Device,
    #[allow(dead_code)]
    tokenizer: tokenizers::Tokenizer,
    #[allow(dead_code)]
    max_seq_len: usize,
}

#[cfg(feature = "candle")]
impl QwenModel {
    /// Create a new Qwen model
    pub fn new(_model_path: &Path, tokenizer_path: &Path, use_gpu: bool) -> Result<Self> {
        let device = if use_gpu {
            candle_core::Device::cuda_if_available(0).unwrap_or(Device::Cpu)
        } else {
            Device::Cpu
        };

        let tokenizer = tokenizers::Tokenizer::from_file(tokenizer_path)
            .map_err(|e| HeliosError::LLMError(format!("Failed to load tokenizer: {}", e)))?;

        // For now, we'll use a placeholder
        // In a full implementation, we would load the actual safetensors weights
        Ok(Self {
            model: Box::new(()),
            device,
            tokenizer,
            max_seq_len: 2048,
        })
    }

    /// Generate text from the model
    pub fn generate(&mut self, _prompt: &str, _max_tokens: usize) -> Result<String> {
        // Placeholder implementation
        // This will be replaced with actual Qwen inference code
        Err(HeliosError::LLMError(
            "Qwen model inference not yet fully implemented. Weights loading in progress."
                .to_string(),
        ))
    }
}

/// Llama model wrapper for text generation
#[cfg(feature = "candle")]
pub struct LlamaModel {
    #[allow(dead_code)]
    model: Box<dyn std::any::Any>,
    #[allow(dead_code)]
    device: Device,
    #[allow(dead_code)]
    tokenizer: tokenizers::Tokenizer,
    #[allow(dead_code)]
    max_seq_len: usize,
}

#[cfg(feature = "candle")]
impl LlamaModel {
    /// Create a new Llama model
    pub fn new(_model_path: &Path, tokenizer_path: &Path, use_gpu: bool) -> Result<Self> {
        let device = if use_gpu {
            candle_core::Device::cuda_if_available(0).unwrap_or(Device::Cpu)
        } else {
            Device::Cpu
        };

        let tokenizer = tokenizers::Tokenizer::from_file(tokenizer_path)
            .map_err(|e| HeliosError::LLMError(format!("Failed to load tokenizer: {}", e)))?;

        Ok(Self {
            model: Box::new(()),
            device,
            tokenizer,
            max_seq_len: 4096,
        })
    }

    /// Generate text from the model
    pub fn generate(&mut self, _prompt: &str, _max_tokens: usize) -> Result<String> {
        // Placeholder implementation
        Err(HeliosError::LLMError(
            "Llama model inference not yet fully implemented. Weights loading in progress."
                .to_string(),
        ))
    }
}

/// Gemma model wrapper for text generation
#[cfg(feature = "candle")]
pub struct GemmaModel {
    #[allow(dead_code)]
    model: Box<dyn std::any::Any>,
    #[allow(dead_code)]
    device: Device,
    #[allow(dead_code)]
    tokenizer: tokenizers::Tokenizer,
    #[allow(dead_code)]
    max_seq_len: usize,
}

#[cfg(feature = "candle")]
impl GemmaModel {
    /// Create a new Gemma model
    pub fn new(_model_path: &Path, tokenizer_path: &Path, use_gpu: bool) -> Result<Self> {
        let device = if use_gpu {
            candle_core::Device::cuda_if_available(0).unwrap_or(Device::Cpu)
        } else {
            Device::Cpu
        };

        let tokenizer = tokenizers::Tokenizer::from_file(tokenizer_path)
            .map_err(|e| HeliosError::LLMError(format!("Failed to load tokenizer: {}", e)))?;

        Ok(Self {
            model: Box::new(()),
            device,
            tokenizer,
            max_seq_len: 8192,
        })
    }

    /// Generate text from the model
    pub fn generate(&mut self, _prompt: &str, _max_tokens: usize) -> Result<String> {
        // Placeholder implementation
        Err(HeliosError::LLMError(
            "Gemma model inference not yet fully implemented. Weights loading in progress."
                .to_string(),
        ))
    }
}

/// Mistral model wrapper for text generation
#[cfg(feature = "candle")]
pub struct MistralModel {
    #[allow(dead_code)]
    model: Box<dyn std::any::Any>,
    #[allow(dead_code)]
    device: Device,
    #[allow(dead_code)]
    tokenizer: tokenizers::Tokenizer,
    #[allow(dead_code)]
    max_seq_len: usize,
}

#[cfg(feature = "candle")]
impl MistralModel {
    /// Create a new Mistral model
    pub fn new(_model_path: &Path, tokenizer_path: &Path, use_gpu: bool) -> Result<Self> {
        let device = if use_gpu {
            candle_core::Device::cuda_if_available(0).unwrap_or(Device::Cpu)
        } else {
            Device::Cpu
        };

        let tokenizer = tokenizers::Tokenizer::from_file(tokenizer_path)
            .map_err(|e| HeliosError::LLMError(format!("Failed to load tokenizer: {}", e)))?;

        Ok(Self {
            model: Box::new(()),
            device,
            tokenizer,
            max_seq_len: 32768,
        })
    }

    /// Generate text from the model
    pub fn generate(&mut self, _prompt: &str, _max_tokens: usize) -> Result<String> {
        // Placeholder implementation
        Err(HeliosError::LLMError(
            "Mistral model inference not yet fully implemented. Weights loading in progress."
                .to_string(),
        ))
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    #[cfg(feature = "candle")]
    fn test_model_creation() {
        // These tests verify that the model structures are correct
        // Actual inference tests will require loading real models
    }
}