Create SmolLM2 vs. GPT-2, Llama2.

SmolLM2 vs. GPT-2, Llama2.

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+OK, starting now. As soon as your report is ready, I'll let you know. In the meantime, feel free to leave this chat.
+SmolLM2 vs. GPT-2, Llama2.
+Analyzing results...
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+Deep Research
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+Canvas
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+Gemini can make mistakes, so double-check it
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+SmolLM2 vs. GPT-2, Llama2.
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+Show thinking
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+Researching SmolLM2 Architecture
+I'm starting a new research task to compare the architecture of SmolLM2-360M with GPT-2 and Llama2. My plan involves a deep dive into SmolLM2's specific architectural choices, including its attention mechanisms, positional encoding, normalization techniques, and activation functions. I'll then conduct a detailed comparative analysis with GPT-2 and Llama2, discussing the implications of these design decisions on performance and efficiency. I'll also extract and compare relevant Python code snippets from each model's implementation.
+Next Steps: Accessing SmolLM2 Code
+My immediate next step is to locate and access the `SmolLM2_360M_model.py` file on Hugging Face to begin analyzing its code. I'll use a search tool to find the correct repository and then a browsing tool to retrieve the file's content.
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+Researching websites
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+huggingface.co
+QuantFactory/SmolLM2-360M-GGUF - Hugging Face
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+modelscope.cn
+SMOLLM CoT 360M GGUF ON CUSTOM SYNTHETIC DATA - 模型详情
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+huggingface.co
+HuggingFaceTB/SmolLM2-360M · Hugging Face
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+dataloop.ai
+SmolLM2 360M · Models - Dataloop AI
+Initial Findings on SmolLM2-360M
+I've successfully located initial information about the SmolLM2-360M model. It's part of a family of compact language models, designed to be lightweight and capable of running on-device, with 360M parameters. It uses a Transformer decoder architecture, similar to GPT-2 and Llama2, and was trained on a massive 4 trillion tokens. I've also found references to an arXiv paper and a GitHub repository, which are crucial for a deeper architectural understanding.
+Next Steps: Deep Dive into SmolLM2 Architecture
+My next steps involve delving into the technical specifics of SmolLM2-360M. I plan to browse the associated arXiv paper for a high-level architectural overview and then navigate the GitHub repository to find the actual Python code defining the model's structure. Once I have the code, I'll analyze its attention mechanism, positional embeddings, normalization layers, and activation functions to prepare for a detailed comparison with GPT-2 and Llama2.
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+Researching websites
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+github.com
+huggingface/smollm: Everything about the SmolLM2 and SmolVLM family of models - GitHub
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+arxiv.org
+arxiv.org
+SmolLM2 Architectural Details Uncovered
+I've made good progress in understanding the SmolLM2-360M architecture by reviewing its arXiv paper. I've confirmed it uses 24 layers, a model dimension of 2,048, an FFN dimension of 8,192, and 32 attention heads. It also employs RoPE for positional embeddings with a base of 10,000, and SwiGLU as its activation function. The model supports a sequence length of 2,048 tokens, extendable to 8k, and utilizes tied embeddings. This provides a solid foundation for comparison with GPT-2 and Llama2.
+Identifying Information Gaps
+While the arXiv paper provided many details, it didn't specify the exact type of attention mechanism (e.g., Multi-Head, Grouped Query, or Multi-Query Attention) or the precise normalization techniques and their placement within the architecture. These are critical details for a comprehensive comparison.
+Next Steps: Code-Level Analysis
+To fill these gaps, my next action is to locate and examine the actual Python code for SmolLM2-360M, specifically the `SmolLM2_360M_model.py` file or its equivalent, within the Hugging Face `smollm` GitHub repository. I will navigate the repository's file structure to find the model definition and then browse its content to extract the missing architectural specifics.
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+Researching websites
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+github.com
+transformers/docs/source/en/model_doc/helium.md at main - GitHub
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+github.com
+huggingface/smollm: Everything about the SmolLM2 and SmolVLM family of models - GitHub
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+github.com
+Error at scatter num_items_in_batch in ddp/dp · Issue #36492 · huggingface/transformers
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+github.com
+ethicalabs-ai/FlowerTune-SmolLM2-360M-Instruct-Finance - GitHub
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+huggingface.co
+HuggingFaceTB/SmolLM2-360M-Instruct · Hugging Face
+Refining SmolLM2 Code Search
+I've encountered a slight challenge in directly locating the `SmolLM2_360M_model.py` file within the `huggingface/smollm` repository. My previous searches didn't yield a direct link to the specific model definition file. However, I've found an indication that SmolLM2 might be integrated into the broader Hugging Face `transformers` library, which is a common practice for widely used models.
+Next Steps: Targeted Code Discovery
+To overcome this, I'm adjusting my strategy. I will now specifically search for the SmolLM2 model definition within the `huggingface/transformers` GitHub repository, anticipating a file path like `transformers/src/transformers/models/smollm/modeling_smollm.py`. Once I locate this file, I will browse its content to extract the precise details regarding its attention mechanism and normalization techniques, which were not fully specified in the arXiv paper. This will allow me to complete my architectural understanding of SmolLM2 before proceeding with the detailed comparison to GPT-2 and Llama2.
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+Researching websites
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+github.com
+transformers/docs/source/en/generation_strategies.md at main - GitHub
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+github.com
+[ModernBERT] Add CausalLM functionality to ModernBERT by orionw · Pull Request #35946 · huggingface/transformers - GitHub
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+huggingface.co
+SmolVLM - Hugging Face
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+self-supervised.cs.jhu.edu
+Transformer Language Models
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+huggingface.co
+Topic 33: Slim Attention, KArAt, XAttention and Multi-Token Attention Explained – What's Really Changing in Transformers? - Hugging Face
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+huggingface.co
+SmolVLM - Hugging Face
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+huggingface.co
+HuggingFaceTB/SmolLM2-1.7B · Hugging Face
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+arxiv.org
+SmolLM2: When Smol Goes Big — Data-Centric Training of a Small Language Model - arXiv
+Inferring SmolLM2's Architecture
+I've been working to pinpoint the exact architectural details of SmolLM2-360M, particularly its attention mechanism and normalization. While I haven't found the direct `SmolLM2_360M_model.py` file, I'm synthesizing information from various sources and common trends in modern LLMs. Given that SmolLM2 uses RoPE and SwiGLU, similar to Llama2, I'm inferring that it likely employs Grouped Query Attention (GQA) or Multi-Query Attention (MQA) for efficiency. Similarly, I'm inferring the use of RMSNorm, probably in a pre-normalization setup, aligning with the efficiency focus of contemporary compact models.
+Addressing Information Gaps
+The primary challenge has been the absence of explicit details on SmolLM2's attention mechanism and normalization in the available documentation. By leveraging my understanding of modern LLM design principles and drawing parallels with models like Llama2, I'm able to make educated inferences about these missing components. This allows me to proceed with a comprehensive architectural comparison, even without direct access to the specific implementation file.
+Next Steps: Comparative Analysis and Conceptual Code
+With these inferences, I now have sufficient information to move forward. My next steps involve conducting a detailed comparative analysis of SmolLM2 (with its inferred architectural choices) against GPT-2 and Llama2. I will then discuss the implications of these architectural decisions. Since I don't have the exact code, I will provide conceptual Python code snippets that illustrate the inferred GQA, RoPE, RMSNorm, and SwiGLU implementations, clearly stating that these are conceptual representations and not direct extracts from SmolLM2's source code.