Spaces:

CrashOverrideX
/

test

Running

App Files Files Community

jd lee commited on May 31

Commit

c8d60db

verified ·

1 Parent(s): 7999f49

Add 3 files

Browse files

Files changed (3) hide show

README.md +6 -4
index.html +40 -17
prompts.txt +1 -0

README.md CHANGED Viewed

@@ -1,10 +1,12 @@
 ---
-title: Test
-emoji: 🦀
 colorFrom: blue
-colorTo: red
 sdk: static
 pinned: false
 ---
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

 ---
+title: test
+emoji: 🐳
 colorFrom: blue
+colorTo: gray
 sdk: static
 pinned: false
+tags:
+  - deepsite
 ---
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

index.html CHANGED Viewed

@@ -1,19 +1,42 @@
-<!doctype html>
 <html>
-	<head>
-		<meta charset="utf-8" />
-		<meta name="viewport" content="width=device-width" />
-		<title>My static Space</title>
-		<link rel="stylesheet" href="style.css" />
-	</head>
-	<body>
-		<div class="card">
-			<h1>Welcome to your static Space!</h1>
-			<p>You can modify this app directly by editing <i>index.html</i> in the Files and versions tab.</p>
-			<p>
-				Also don't forget to check the
-				<a href="https://huggingface.co/docs/hub/spaces" target="_blank">Spaces documentation</a>.
-			</p>
-		</div>
-	</body>
 </html>

+<!DOCTYPE html>
 <html>
+  <head>
+    <title>My app</title>
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <meta charset="utf-8">
+    <style>
+      body {
+        display: flex;
+        justify-content: center;
+        align-items: center;
+        overflow: hidden;
+        height: 100dvh;
+        font-family: "Arial", sans-serif;
+        text-align: center;
+        background-color: #fff;
+      }
+      .arrow {
+        position: absolute;
+        bottom: 32px;
+        left: 0px;
+        width: 100px;
+        transform: rotate(30deg);
+      }
+      h1 {
+        font-size: 50px;
+      }
+      h1 span {
+        color: #acacac;
+        font-size: 32px;
+      }
+    </style>
+  </head>
+  <body>
+    <h1>
+      <span>I'm ready to work,</span><br />
+      Ask me anything.
+    </h1>
+    <img src="https://enzostvs-deepsite.hf.space/arrow.svg" class="arrow" />
+    <script></script>
+  <p style="border-radius: 8px; text-align: center; font-size: 12px; color: #fff; margin-top: 16px;position: fixed; left: 8px; bottom: 8px; z-index: 10; background: rgba(0, 0, 0, 0.8); padding: 4px 8px;">Made with <img src="https://enzostvs-deepsite.hf.space/logo.svg" alt="DeepSite Logo" style="width: 16px; height: 16px; vertical-align: middle;display:inline-block;margin-right:3px;filter:brightness(0) invert(1);"><a href="https://enzostvs-deepsite.hf.space" style="color: #fff;text-decoration: underline;" target="_blank" >DeepSite</a> - 🧬 <a href="https://enzostvs-deepsite.hf.space?remix=userdatax/test" style="color: #fff;text-decoration: underline;" target="_blank" >Remix</a></p></body>
 </html>

prompts.txt ADDED Viewed

	@@ -0,0 +1 @@

+ # Comprehensive AI Fine-Tuning Platform with Local GGUF Support and Playground Integration This comprehensive technical analysis presents a detailed plan for developing a robust AI fine-tuning platform that integrates local GGUF model support, provides an interactive playground environment, and incorporates advanced optimization techniques. The platform leverages cutting-edge quantization methods and neural scaling laws to deliver optimal performance while maintaining energy efficiency and security standards. ## Platform Architecture and Core Framework The foundation of this AI fine-tuning platform centers on a modular architecture that seamlessly integrates GGUF model handling with advanced fine-tuning capabilities. The GGUF format serves as the cornerstone, offering significant advantages for local deployment due to its binary structure optimized for fast loading and saving of models[1][10]. Unlike traditional tensor-only formats, GGUF encodes both tensors and standardized metadata, making it highly efficient for inference purposes[10]. This architectural decision ensures that the platform can handle quantized models ranging from 2 bits to 8 bits using K-quantization methods[14]. The core framework incorporates multiple quantization techniques to maximize efficiency while preserving model performance. Research demonstrates that GGML and its successor GGUF represent the most energy-efficient quantization methods available[1]. The platform will integrate support for Gradient-based Post-Training Quantization (GPTQ), Activation-aware Weight Quantization (AWQ), and the more advanced EXL2 format for GPU-optimized deployments[1][14]. This multi-format approach allows users to select the optimal quantization method based on their hardware constraints and performance requirements. The architectural design prioritizes modularity and extensibility, incorporating established tools like llama.cpp for GGUF processing and Axolotl for comprehensive fine-tuning operations. Axolotl's support for various Hugging Face models including Llama, Pythia, Falcon, and MPT, combined with its capability to handle full fine-tuning, LoRA, QLoRA, and ReLoRA techniques, makes it an ideal foundation for the platform's fine-tuning engine[8]. The integration of these components ensures that users can transition seamlessly between different model formats and optimization techniques without compromising functionality. ## GGUF Integration and Local Model Management The local model management system forms a critical component of the platform, specifically designed to handle GGUF files efficiently while addressing security concerns. Recent research has identified significant vulnerabilities in GGUF file parsing, including heap overflows that could be exploited through crafted files[2]. The platform addresses these security risks by implementing robust validation mechanisms and sandboxed parsing environments that prevent potential exploits from compromising system integrity. The model management interface provides comprehensive support for GGUF metadata parsing and tensor information visualization. Using the @huggingface/gguf JavaScript parser, the platform offers remote file handling capabilities that allow users to examine model metadata and tensor specifications before local deployment[10]. This functionality extends to automatic detection of local models in Hugging Face cache directories, streamlining the workflow for users working with multiple model variants and quantization levels. Local GGUF file processing incorporates advanced caching mechanisms and intelligent prefetching to optimize loading times and memory usage. The platform leverages llama.cpp's native GGUF support, which enables direct model loading through Hugging Face repository paths and automatic caching controlled by the LLAMA_CACHE environment variable[3]. This integration ensures that users can efficiently manage large model collections while maintaining optimal disk space utilization through intelligent cache management policies. The system implements comprehensive model versioning and provenance tracking, maintaining detailed records of quantization parameters, source models, and fine-tuning history. This metadata management approach ensures reproducibility and enables users to track model evolution throughout the development process. Additionally, the platform supports conversion utilities for transforming existing PyTorch models to GGUF format using tools like ggml-org/gguf-my-repo, expanding compatibility with existing model ecosystems[10]. ## Fine-tuning Framework Implementation The fine-tuning framework represents the platform's most sophisticated component, incorporating state-of-the-art techniques for efficient model adaptation while working with quantized GGUF models. The implementation centers on QLoRA (Quantized Low-Rank Adaptation), which enables fine-tuning of large parameter models on consumer hardware by backpropagating gradients through frozen, 4-bit quantized pretrained language models into Low Rank Adapters[20]. This approach allows fine-tuning of 65B parameter models on single 48GB GPUs while preserving full 16-bit fine-tuning performance[20]. The framework integrates multiple quantization innovations to maximize memory efficiency without sacrificing performance. These include 4-bit NormalFloat (NF4), an information-theoretically optimal data type for normally distributed weights, double quantization to reduce memory footprint by quantizing the quantization constants, and paged optimizers to manage memory spikes[20]. The platform's implementation ensures that these optimizations work seamlessly with GGUF format models, providing users with the most advanced fine-tuning capabilities available. For users working with local GGUF files, the platform provides specialized fine-tuning capabilities through llama.cpp's integrated finetune utility. This CPU-optimized approach dramatically reduces hardware requirements while maintaining compatibility with quantized models[5]. The system supports both CPU and CUDA-accelerated fine-tuning, automatically detecting available hardware and optimizing the training pipeline accordingly. This flexibility ensures that users can fine-tune models regardless of their hardware configuration, from laptop CPUs to high-end GPU clusters. The fine-tuning pipeline incorporates advanced dataset preprocessing and validation mechanisms, supporting multiple input formats including custom tokenized datasets. Integration with frameworks like Axolotl provides comprehensive configuration management through YAML files, enabling users to specify training parameters, model architectures, and optimization strategies with precision[8]. The platform's monitoring capabilities include real-time training metrics, loss visualization, and automatic checkpointing to prevent data loss during extended training sessions. ## LLM Playground Interface and Real-time Interaction The integrated playground interface provides users with a comprehensive environment for testing and iterating on their fine-tuned models before production deployment. Drawing inspiration from successful implementations like OpenPlayground, the interface supports multiple model backends including OpenAI-compatible APIs, local GGUF models through llama.cpp server integration, and direct model inference[6]. This multi-backend approach ensures that users can compare performance across different deployment scenarios and optimization levels. The playground incorporates advanced features for model comparison and parameter tuning, enabling side-by-side evaluation of different models with identical prompts. Users can individually adjust model parameters including temperature, top-p sampling, and context length limits, providing granular control over model behavior[6]. The interface maintains comprehensive interaction history, allowing users to track model performance across different parameter configurations and identify optimal settings for specific use cases. Real-time inference capabilities leverage the efficiency of GGUF format models, providing near-instantaneous responses for most consumer hardware configurations. The platform integrates voice interaction capabilities using SpeechRecognition for input and text-to-speech synthesis for output, creating a comprehensive multimodal interface[9]. This voice integration extends the platform's utility beyond traditional text-based interactions, enabling natural language model testing and evaluation workflows. The playground environment includes advanced prompt engineering tools, template management systems, and conversation flow analysis. Users can save and share prompt templates, analyze model responses for consistency and accuracy, and export conversation logs for further analysis[9]. Integration with the fine-tuning framework allows users to identify areas where additional training data might improve model performance, creating a seamless feedback loop between testing and optimization phases. ## Performance Optimization and Neural Scaling Implementation The platform's performance optimization strategy incorporates neural scaling laws to guide model selection and training decisions. The Chinchilla scaling law provides a mathematical framework for optimizing the relationship between model parameters, training data, and computational cost[7]. The implementation uses the scaling relationship: $$ L = \frac{A}{N^{\alpha}} + \frac{B}{D^{\beta}} + L_0 $$ where $$L$$ represents the average negative log-likelihood loss, $$N$$ is the number of parameters, $$D$$ is the number of training tokens, and the constants $$A$$, $$B$$, $$\alpha$$, $$\beta$$, and $$L_0$$ are empirically determined[7]. The platform uses this formula to recommend optimal model sizes and training data requirements based on available computational resources and target performance metrics. Energy efficiency optimization represents a critical consideration in the platform's design, particularly given the environmental impact of large-scale AI training and inference. Research comparing quantization methods reveals significant variability in energy profiles, challenging assumptions about universal efficiency improvements from lower precision[1]. The platform incorporates energy monitoring capabilities that track power consumption during fine-tuning and inference operations, providing users with detailed efficiency metrics to guide optimization decisions. The performance optimization engine implements intelligent resource allocation algorithms that dynamically adjust between CPU and GPU utilization based on model size, quantization level, and available hardware. For GGUF models, the platform leverages the format's inherent efficiency advantages, including fast loading times and optimized memory layouts[12]. Advanced caching strategies ensure that frequently accessed model components remain in memory while less critical elements are efficiently swapped to storage as needed. Model deployment optimization includes automatic selection of optimal quantization levels based on target hardware specifications and performance requirements. The platform's benchmarking capabilities provide comprehensive performance metrics across different quantization methods, enabling users to make informed decisions about deployment strategies[1]. Integration with container orchestration systems allows for scalable deployment across multiple hardware configurations while maintaining consistent performance characteristics. ## Security and Robustness Considerations Security implementation addresses the critical vulnerabilities identified in GGUF file parsing, particularly the heap overflow vulnerabilities that can be exploited through maliciously crafted files[2]. The platform implements comprehensive input validation, sandboxed model loading environments, and strict bounds checking to prevent potential exploits. These security measures include verification of magic values, careful validation of tensor dimensions and metadata, and isolation of model parsing operations from critical system components. The robustness framework incorporates comprehensive error handling and recovery mechanisms designed to handle corrupted models, incomplete fine-tuning operations, and hardware failures gracefully. Automatic checkpoint creation ensures that training progress is preserved even during unexpected system interruptions, while intelligent recovery algorithms can resume operations from the most recent stable state. The platform maintains detailed logging of all operations, enabling forensic analysis of any issues that might arise during model development or deployment. Data privacy protection forms a cornerstone of the platform's design philosophy, ensuring that all model training and inference operations can be performed entirely offline when required. The local processing capability eliminates dependencies on external services for sensitive applications, while encryption mechanisms protect model files and training data at rest and in transit[11]. This privacy-preserving approach makes the platform suitable for applications involving confidential or regulated data. Quality assurance mechanisms include automated testing of model outputs, consistency validation across different quantization levels, and performance regression detection. The platform implements comprehensive evaluation frameworks that assess model behavior across multiple dimensions, including accuracy, consistency, bias detection, and adherence to specified output formats[15]. These quality controls ensure that fine-tuned models maintain expected performance characteristics throughout the development and deployment lifecycle. ## Implementation Roadmap and Technical Specifications The development roadmap prioritizes core functionality delivery through phased implementation, beginning with basic GGUF model loading and playground interface development. Phase one focuses on establishing the foundational infrastructure including secure GGUF parsing, basic inference capabilities, and a minimal viable playground interface. This initial phase ensures that users can immediately begin working with local GGUF models while more advanced features are developed in subsequent phases. Phase two implementation centers on fine-tuning framework integration, incorporating QLoRA support, Axolotl integration, and advanced quantization techniques. This phase includes development of the training pipeline, checkpoint management systems, and real-time monitoring capabilities. Special attention is given to ensuring compatibility between fine-tuning outputs and the GGUF ecosystem, enabling seamless transitions between training and deployment phases. The final implementation phase focuses on advanced optimization features, including neural scaling law implementation, energy efficiency monitoring, and automated hyperparameter optimization. This phase also includes comprehensive security hardening, performance optimization, and integration testing across diverse hardware configurations. The development approach emphasizes iterative testing and user feedback integration to ensure that the platform meets real-world requirements for AI model development and deployment. Technical specifications include support for modern hardware accelerators, comprehensive API development for programmatic access, and extensive documentation covering both user workflows and developer integration patterns. The platform's architecture ensures scalability from single-user laptop deployments to enterprise-scale model development environments, providing flexibility for diverse use cases and organizational requirements. ## Conclusion This comprehensive AI fine-tuning platform represents a significant advancement in local model development capabilities, combining the efficiency of GGUF format optimization with state-of-the-art fine-tuning techniques and comprehensive security measures. The integration of neural scaling laws, energy efficiency optimization, and advanced quantization methods creates a robust foundation for developing high-performance language models while minimizing computational requirements and environmental impact. The platform's modular architecture ensures that users can leverage the most appropriate tools and techniques for their specific requirements, whether developing specialized domain models or experimenting with novel architectural approaches. The combination of local processing capabilities, comprehensive security measures, and advanced optimization techniques positions this platform as a valuable tool for researchers, developers, and organizations seeking to harness the power of large language models while maintaining control over their data and computational resources. The implementation roadmap provides a clear path toward delivering a production-ready platform that addresses current limitations in AI model development workflows while anticipating future needs for scalability, security, and performance optimization. Through careful integration of established tools and cutting-edge research, this platform will enable more efficient and accessible AI model development across diverse applications and use cases. Citations: [1] https://ieeexplore.ieee.org/document/10628367/ [2] https://www.databricks.com/blog/ggml-gguf-file-format-vulnerabilities [3] https://huggingface.co/docs/hub/en/gguf-llamacpp [4] https://www.reddit.com/r/LocalLLaMA/comments/17hml70/how_can_i_finetune_local_gguf_llama_2_model/ [5] https://docs.gaianet.ai/creator-guide/finetune/llamacpp/ [6] https://github.com/keldenl/openplayground [7] https://en.wikipedia.org/wiki/Neural_scaling_law [8] https://github.com/axolotl-ai-cloud/axolotl [9] https://ijsrem.com/download/design-and-implementation-of-a-fine-tuned-llama-based-ai-chatbot-with-voice-and-text-interaction-using-streamlit-and-ollama/ [10] https://huggingface.co/docs/hub/en/gguf [11] https://www.semanticscholar.org/paper/bcad5bc74a04e1f8571130caf4a718774b86fd44 [12] https://www.ibm.com/think/topics/gguf-versus-ggml [13] https://www.semanticscholar.org/paper/caf381b67335593a763e6ccb5ac6b8e7dbbd9def [14] https://www.reddit.com/r/LocalLLaMA/comments/1ayd4xr/for_those_who_dont_know_what_different_model/ [15] https://www.semanticscholar.org/paper/5ec2aa6c84e4b7ee51c6cc3e4cd74ed3b21c2df1 [16] https://cca.informatik.uni-freiburg.de/debugging/ws23/FORMAT.html [17] https://link.springer.com/10.1140/epjc/s10052-023-11780-9 [18] https://link.springer.com/10.1007/s00418-023-02209-1 [19] https://www.medra.org/servlet/aliasResolver?alias=iospressISBN&isbn=978-1-61499-648-4&spage=87&doi=10.3233/978-1-61499-649-1-87 [20] https://www.semanticscholar.org/paper/32ac52069e562d4f900afee70bdca63f53461481 [21] https://academic.oup.com/bioinformatics/article/25/16/2078/204688 [22] https://www.aclweb.org/anthology/2020.findings-emnlp.171 [23] https://huggingface.co/docs/transformers/en/gguf [24] https://huggingface.co/TheBloke/Yi-6B-GGUF/discussions/2 [25] https://platform.openai.com/docs/guides/fine-tuning [26] https://www.together.ai [27] https://www.semanticscholar.org/paper/e467135b5de441fe6387f41782eb72ef8433057c [28] https://github.com/kyo-takano/chinchilla [29] https://giftoolscookbook.readthedocs.io/en/latest/content/fundamentals/Beta.html [30] https://ieeexplore.ieee.org/document/10851618/ [31] https://www.semanticscholar.org/paper/6e64321a4781c1e6d3d9471b310be1d6192570da [32] https://dataloop.ai/library/model/thebloke_orca_llama_70b_qlora-gguf/ [33] https://llm.extractum.io/model/ewof%2Fkoishi-8x7b-qlora-gguf,3rbADaUSFnwCEqC86h4QQ5 [34] https://huggingface.co/TheBloke/Llama-2-7B-GGUF [35] https://www.semanticscholar.org/paper/88cd6e0f7c2f1fe2bd4796a6729447d60bfeffc9 [36] https://bmcneurosci.biomedcentral.com/articles/10.1186/1471-2202-14-3 [37] https://www.semanticscholar.org/paper/e18a0ef6c6e8b7b0c9d685f86b05a841e3473a88 [38] https://www.semanticscholar.org/paper/0476750c048abe336b9a24dbfa60b975bf0834c1 [39] https://www.semanticscholar.org/paper/9556df494e8c06f99d736545acf4bcc3f58a663c [40] https://www.semanticscholar.org/paper/fea2a0edda9d47ada07e111f8a6bec0582e295d4 [41] https://www.nature.com/articles/s41598-024-64827-6 [42] https://www.semanticscholar.org/paper/e08c1e013681c82a65dd971bfd86d5ae4b48318f [43] https://www.semanticscholar.org/paper/7260442ef9c0448f07ce3803efd49cebaffcebe9 [44] https://link.springer.com/10.1007/s42001-024-00345-9 [45] https://unsloth.ai [46] https://finetunedb.com [47] https://www.reddit.com/r/LocalLLaMA/comments/18qwx8v/open_source_tools_to_fine_tune_your_llm_models/ [48] https://www.entrypointai.com [49] https://theresanaiforthat.com/ai/localai/ [50] https://dataloop.ai/library/model/thebloke_llama-2-7b-guanaco-qlora-gguf/ [51] https://www.semanticscholar.org/paper/5e71d0e85f65a1c0fb2af7bff281209122c58932 [52] https://www.semanticscholar.org/paper/2b5e40c9c6c76569714b902a53838cb80ce89a26 [53] https://www.semanticscholar.org/paper/01f159e5d11620a4fa57e3fafd5019df2babbc97 [54] https://www.semanticscholar.org/paper/f3d7fdcdb1191a4a59e78f54d67fcd46a3462d2b [55] https://www.semanticscholar.org/paper/fce434329773bdec3deb7e5985c1780492f8cd6f [56] https://www.semanticscholar.org/paper/283473ce704a6cc2c547fe6719d53622615e8e57 [57] https://www.semanticscholar.org/paper/041fcaa32a8dc362bd2644f575950fea042e4c53 [58] https://www.semanticscholar.org/paper/f9982586bb0f2c40d3d92e18c0a0b0723305e5a2 [59] https://cameronrwolfe.substack.com/p/llm-scaling-laws [60] https://arxiv.org/abs/2001.08361 [61] https://blogs.nvidia.com/blog/ai-scaling-laws/ [62] https://substack.com/@cwolferesearch/note/c-86026546 [63] https://www.envisioning.io/vocab/chinchilla-scaling [64] https://or.stackexchange.com/questions/8322/how-to-calculate-the-trade-off-between-objectives-in-multi-objective-optimizatio [65] https://klu.ai/glossary/scaling-laws [66] https://ieeexplore.ieee.org/document/10704454/ [67] https://aclanthology.org/2023.wmt-1.43 [68] https://ieeexplore.ieee.org/document/10700744/ [69] https://www.semanticscholar.org/paper/16a2140ab8470b66f48bd4c817f1052afe6f48f5 [70] https://ieeexplore.ieee.org/document/10838185/ [71] https://ierj.in/journal/index.php/ierj/article/view/3345 [72] https://ejurnal.itenas.ac.id/index.php/mindjournal/article/view/12732 [73] https://www.reddit.com/r/LocalLLaMA/comments/18qrb0k/feasibility_and_meaningfulness_of_a_project_on/ [74] https://github.com/ggerganov/llama.cpp/discussions/3489 [75] https://huggingface.co/TheBloke/llama-2-7B-Guanaco-QLoRA-GGUF [76] https://huggingface.co/TheBloke/Mistral-7B-Code-16K-qlora-GGUF [77] http://docs.axolotl.ai [78] https://medium.aiplanet.com/no-code-llm-fine-tuning-using-axolotl-2db34e3d0647 --- Answer from Perplexity: pplx.ai/share