mistral tell me about mistral and "multitoken" models https://chat.mistral.ai/chat?q=what%20is%20mistral.ai%20and%20le%20chat%20wh... what is mistral.ai and le chat what are they Mistral AI is a French startup specializing in developing large language models (LLMs). Founded in April 2023 by former researchers from Google DeepMind and Meta, the company is headquartered in Paris and has quickly established itself as a significant player in the AI industry. Mistral AI focuses on creating open-source, customizable, and cost-effective AI models that can be deployed across various platforms, including on-premises, cloud, edge devices, and data centers. The company has secured substantial funding from prominent investors and has a reported valuation of $6 billion123. Le Chat is an AI assistant developed by Mistral AI. It offers a range of features, including web search, image generation, and real-time updates. Le Chat is designed to be a comprehensive tool for both personal and professional use, with capabilities such as planning daily activities, tracking projects, and summarizing documents. The assistant is available on iOS and Android, and it will soon be accessible on private infrastructure for enterprises. Le Chat also includes a "canvas" feature that allows users to collaborate with Mistral's AI models on shared outputs, enabling ideation and creation for knowledge work1456. how does the development of le chat compare to that of other mainstream models? is it ahead or behind? are the weights public? is the architecture and training regimen public? Le Chat, developed by Mistral AI, is a comprehensive and multilingual conversational assistant based on large language models. It is designed to enhance both personal and professional productivity with features like project tracking, document summarization, and real-time information sourcing7. Comparison to Other Mainstream Models Performance: Le Chat is powered by Mistral's advanced language models, such as Mistral Large and Pixtral Large. These models have shown impressive performance, with Mistral Large scoring 81.2% on the MMLU benchmark, positioning it just behind GPT-4, which scores 86.4%. This places Mistral Large as the second-best performing model on this benchmark, ahead of other models like Claude 2, Gemini Pro, and LLaMA 28. Features: Le Chat offers a range of features that make it competitive with other mainstream models. It includes capabilities like web search, image generation through a partnership with Flux Pro, and a "canvas" feature for collaborative ideation. These features position Le Chat as a versatile AI assistant, capable of handling tasks traditionally requiring multiple tools5910. Speed and Efficiency: One of Le Chat's standout features is its speed. It processes information at approximately 1,000 words per second, which is faster than other chat assistants like ChatGPT and Anthropic’s AI model Claude11. Multilingual Capabilities: Le Chat demonstrates robust multilingual capabilities, with fluency in multiple languages including French, English, German, Spanish, and Italian. This multilingual foundation sets it apart from competitors, as the model was specifically designed with multilingualism as a core feature rather than an afterthought11. Weights, Architecture, and Training Regimen Weights: Mistral AI models, including those powering Le Chat, are available under the Apache 2.0 license, making both weights and code sources available. This transparency appeals to enterprises that have compliance and regulatory requirements. Architecture and Training: Mistral AI is known for its open-source and customizable models. The architecture of Mistral's models is designed to be flexible and efficient, with a focus on low latency and high throughput. The models are trained on diverse datasets to ensure robust performance across various tasks. Mistral AI's commitment to open-source development means that the architecture and training regimen are publicly available, allowing researchers and developers to fine-tune and customize the models for specific use cases3. In summary, Le Chat is a highly competitive AI assistant that leverages Mistral AI's advanced language models to offer a comprehensive set of features. Its performance, speed, and multilingual capabilities make it a strong contender in the AI market, while its open-source nature and transparent development process set it apart from other mainstream models. how does it generate tokens faster? what's the engineering approach? Le Chat, developed by Mistral AI, achieves rapid token generation through a combination of advanced engineering approaches and innovative technologies. Here’s how it manages to generate tokens faster: Wafer Scale Engine Technology: Le Chat leverages Cerebras' Wafer Scale Engine technology, which is designed to accelerate AI inference. This technology employs a three-tier memory system (SRAM, HBM, and DDR) that allows for reconfigurable dataflow and tensor parallelism. This setup enables high generation speeds, achieving over 1,100 tokens per second, which is significantly faster than other popular models like ChatGPT 4o, Sonnet 3.5, and DeepSeek R11213. Speculative Decoding: In collaboration with researchers at Mistral, Cerebras has developed speculative decoding techniques. These techniques help in predicting and generating tokens more efficiently by anticipating the next likely tokens in a sequence. This reduces the time needed to generate each token, contributing to the overall speed of the model13. SRAM-Based Inference Architecture: The Wafer Scale Engine 3 used by Le Chat features an SRAM-based inference architecture. SRAM (Static Random-Access Memory) is known for its fast access times, which is crucial for quick token generation. By using SRAM, the system can retrieve and process data more swiftly, leading to faster inference times13. Optimized Hardware and Software Integration: The integration of advanced hardware like the Wafer Scale Engine with optimized software techniques ensures that Le Chat can handle token generation efficiently. This combination allows for fast first token times and maintains high throughput, which is essential for real-time applications like chat and code generation1213. These engineering approaches collectively enable Le Chat to achieve impressive token generation speeds, making it one of the fastest AI assistants available. Who was the first to deploy speculative decoding at scale, and why do you think it wasn't done earlier? Speculative decoding is an optimization technique for inference that makes educated guesses about future tokens while generating the current token, all within a single forward pass. It incorporates a verification mechanism to ensure the correctness of these speculated tokens, thereby guaranteeing that the overall output of speculative decoding is identical to that of vanilla decoding14. First to Deploy Speculative Decoding at Scale The available information does not explicitly identify the first entity to deploy speculative decoding at scale. However, several organizations and research groups have been instrumental in advancing and implementing speculative decoding techniques. For instance, Google has integrated speculative decoding into its AI Overviews in Google Search to produce results faster while maintaining response quality15. Additionally, companies like Cerebras have deployed speculative decoding in production environments, achieving significant speedups in language models13. Why It Wasn't Done Earlier The delay in the widespread adoption of speculative decoding can be attributed to several factors: Computational Complexity: Speculative decoding requires additional computational resources to manage the draft model and the verification process. This increased complexity can be a barrier to implementation, especially in resource-constrained environments16. Technological Maturity: The technology and infrastructure needed to support speculative decoding at scale have only recently become available. Advances in hardware, such as the Wafer Scale Engine, and optimized software techniques have made it more feasible to deploy speculative decoding efficiently13. Research and Development: The development of speculative decoding techniques has been an ongoing process. Early research focused on proving the concept and demonstrating its efficacy. It took time for these techniques to be refined and optimized for large-scale deployment15. Integration Challenges: Integrating speculative decoding into existing systems can be challenging. It requires modifications to the inference pipeline and careful tuning to ensure that the benefits outweigh the additional complexity14. In summary, while the exact first deployer of speculative decoding at scale is not explicitly mentioned, the technique has been adopted by several leading organizations. The delay in its widespread adoption can be attributed to computational complexity, the need for technological maturity, ongoing research and development, and integration challenges. I think it's because the businesses and the researchers and hackers aren't that tight and maybe don't want to be, but I don't know. How does speculative decoding compare to the multi-token prediction deepseek advertises? Speculative decoding and multi-token prediction (MTP) are both techniques aimed at improving the efficiency of language model inference, but they operate differently and have distinct advantages. Speculative Decoding Speculative decoding is an optimization technique that uses a smaller, faster "draft" model to generate preliminary predictions of multiple tokens. These predictions are then verified by a larger, more powerful main model. The draft model acts as a first pass, producing potential continuations of the text, which the main model either confirms or refines. This process allows the system to generate multiple tokens in a single forward pass, reducing latency and improving overall inference speed14. Multi-Token Prediction (MTP) Multi-Token Prediction (MTP) is a training objective that aims to improve model performance by predicting multiple tokens simultaneously rather than one token at a time. This approach mitigates the distributional discrepancy between training-time teacher forcing and inference-time autoregressive generation. During inference, MTP can facilitate speculative decoding by generating multiple token predictions in parallel, significantly reducing latency and speeding up response times1718. Comparison Parallelism: Both techniques leverage parallelism to speed up token generation. Speculative decoding uses a draft model to generate multiple tokens, which are then verified by the main model. MTP, on the other hand, predicts multiple tokens simultaneously during training and can be used for speculative decoding during inference to improve generation speed1918. Integration: Speculative decoding can be integrated with various models, including those that use MTP. DeepSeek, for example, combines MTP with speculative decoding to achieve a decoding speed 1.8 times faster than traditional methods. This integration allows for more efficient inference, making the model more suitable for real-time applications2021. Efficiency: Both techniques aim to improve the efficiency of language model inference. Speculative decoding reduces the computational load by using a smaller draft model for initial predictions, while MTP enhances training efficiency by predicting multiple tokens simultaneously. When combined, these techniques can significantly accelerate token generation and reduce latency1918. In summary, while speculative decoding and MTP have different approaches, they share the goal of improving the efficiency of language model inference. Speculative decoding focuses on using a draft model to generate preliminary predictions, while MTP predicts multiple tokens simultaneously. When integrated, these techniques can achieve significant speedups in token generation, making them highly effective for real-time applications. has anyone integrated them? it sounds a little like MTP trains a large model similarly to the draft model? is that an incorrect conclusion? you describe speculative decoding as generating multiple tokens and then verifying them -- given they are passed through a model again, wouldn't it be more effective to generate embeddings? Integration of Speculative Decoding and Multi-Token Prediction (MTP) Integration: Speculative decoding and MTP have been integrated to enhance the efficiency of language model inference. DeepSeek, for example, combines MTP with speculative decoding to achieve significant speedups in token generation. During inference, the MTP modules can be used for speculative decoding, which involves generating multiple token predictions in parallel and then verifying them. This integration allows for more efficient inference, making the model more suitable for real-time applications2021. Speculative Decoding with Embeddings Embeddings in Speculative Decoding: Speculative decoding can be optimized by using embeddings instead of generating tokens directly. This approach involves using the embedding vectors from the main model to carry out the guesswork for the next predicted token. The draft model can harness the information in these embeddings to generate preliminary predictions, which are then verified by the main model. This method reduces the computational overhead and improves the efficiency of the speculative decoding process2223. Comparison with MTP MTP and Draft Model Training: MTP trains a large model to predict multiple tokens simultaneously, which is similar to how a draft model is trained in speculative decoding. However, MTP focuses on improving the training efficiency by predicting multiple tokens in a single forward pass, while speculative decoding uses a smaller draft model to generate preliminary predictions that are then verified by the main model1723. Effectiveness of Embeddings Embeddings vs. Token Generation: Generating embeddings instead of tokens can be more effective in speculative decoding because embeddings contain rich contextual information that can be leveraged by the draft model to make more accurate predictions. This approach reduces the need to pass tokens through the model multiple times, thereby improving the overall efficiency of the inference process2223. In summary, speculative decoding and MTP have been integrated to enhance the efficiency of language model inference. Using embeddings in speculative decoding can further optimize the process by leveraging the contextual information contained in the embedding vectors. This approach improves the accuracy and efficiency of the draft model's predictions, making the inference process more effective. Let's try to get on the same page. Here's an outline description of a normal llm model as i see it: 1. embeddings, a lookup matrix 2. transformer 3. linear matrix outputting logits for 1 token Compare to something like seq2seq: 1. input embeddings for seq1, a lookup matrix 2. transformer 3. linear matrix outputting embeddings for all seq1 tokens 4. input embeddings for seq2 5. transformer crossing 3 & 4 6. linear matrix outputting logits for 1 token of seq2 does this seem accurate? what would speculative decoding and mtp look like? keep it concise and rigorous Normal LLM Model Embeddings: A lookup matrix that converts input tokens into dense vectors. Transformer: Processes the embeddings to capture contextual information. Linear Matrix: Outputs logits for the next token based on the transformer's output. Seq2Seq Model Input Embeddings for Seq1: Converts input sequence tokens into dense vectors. Transformer: Processes the embeddings of the input sequence. Linear Matrix: Outputs embeddings for all tokens in the input sequence. Input Embeddings for Seq2: Converts the target sequence tokens into dense vectors. Transformer: Processes the embeddings of the target sequence, incorporating information from the input sequence. Linear Matrix: Outputs logits for one token of the target sequence. Speculative Decoding Embeddings: Converts input tokens into dense vectors. Draft Model: A smaller, faster model that generates multiple preliminary tokens. Main Model: Verifies the draft tokens, accepting or refining them. Linear Matrix: Outputs logits for the verified tokens. Multi-Token Prediction (MTP) Embeddings: Converts input tokens into dense vectors. Transformer: Processes the embeddings to capture contextual information. MTP Modules: Predict multiple tokens simultaneously. Linear Matrix: Outputs logits for the predicted tokens. Integrated Speculative Decoding with MTP Embeddings: Converts input tokens into dense vectors. Draft Model with MTP: Generates multiple preliminary tokens using MTP. Main Model: Verifies the draft tokens, accepting or refining them. Linear Matrix: Outputs logits for the verified tokens. This outline provides a concise and rigorous comparison of the architectures and processes involved in normal LLM models, seq2seq models, speculative decoding, and MTP. this is not clear. let's go to speculative decoding, the first new one you generated. can you remove the word "model" by instead describing the parts of the architecture that are or are not a transformer similar to that from the attention is all you need paper? Speculative Decoding Architecture Embeddings: Converts input tokens into dense vectors using a lookup matrix. Draft Transformer: A smaller, faster transformer that generates multiple preliminary tokens. Main Transformer: Verifies the draft tokens, accepting or refining them. Linear Layer: Outputs logits for the verified tokens. This description avoids using the term "model" and instead focuses on the specific components of the architecture, similar to the description in the "Attention is All You Need" paper. what are the inputs and outputs of each transformer Speculative Decoding Architecture with Inputs and Outputs Embeddings: Input: Raw input tokens. Output: Dense vector representations of the input tokens. Draft Transformer: Input: Dense vector representations from the embeddings layer. Output: Multiple preliminary token embeddings generated in parallel. Main Transformer: Input: Preliminary token embeddings from the Draft Transformer. Output: Verified token embeddings, which are either accepted or refined. Linear Layer: Input: Verified token embeddings from the Main Transformer. Output: Logits for the final tokens. This description clarifies the inputs and outputs for each component in the speculative decoding architecture. what is the output shape of the draft transformer? if a normal llm transformer has an input shape of batch x seq x dim, then its output shape may be batch x seq x dim as well. because of causal masking, only the last seq element is used and we get an output of batch x dim, representing the underlying information needed to predict 1 following token. how does the draft transformer look? Draft Transformer Output Shape In speculative decoding, the Draft Transformer generates multiple preliminary tokens in parallel. Assuming a normal LLM transformer has an input shape of (batch, seq, dim) and an output shape of (batch, seq, dim), the Draft Transformer's output shape will differ to accommodate the generation of multiple tokens. Draft Transformer Details Input Shape: (batch, seq, dim): This is the same as a normal LLM transformer, where batch is the batch size, seq is the sequence length, and dim is the dimensionality of the embeddings. Output Shape: (batch, seq + n, dim): Here, n is the number of additional preliminary tokens generated by the Draft Transformer. The output shape includes the original sequence length seq plus the n additional tokens. Explanation Causal Masking: In a normal LLM transformer, causal masking ensures that each position in the sequence can only attend to previous positions. The output for the last token in the sequence (seq) is used to predict the next token, resulting in an output shape of (batch, dim) for the final token. Draft Transformer: The Draft Transformer generates n additional tokens in parallel. These tokens are preliminary predictions that will be verified by the Main Transformer. The output shape of (batch, seq + n, dim) reflects the generation of these additional tokens. Example If the input sequence length seq is 10 and the Draft Transformer generates 3 additional tokens (n = 3), the output shape will be (batch, 13, dim). This output includes the embeddings for the original 10 tokens plus the 3 additional preliminary tokens. This description clarifies how the Draft Transformer's output shape accommodates the generation of multiple preliminary tokens in speculative decoding. this sounds like an architecture where the input sequence would be padded with n additional elements that serve as placeholders for the additionally generated tokens?