20 results for “topic:phi2”
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Grimoire is All You Need for Enhancing Large Language Models
A branch of sgminer optimized with GCN cross lane instructions on AMD (ethash, phi2, lyra2Z[z], allium, x25x, lyra2REv2/v3, argon2d, yescrypt, neoscrypt, 0x10)
onenm_local_llm is a Flutter plugin that simplifies on-device language model inference on Android using llama.cpp. It removes the complexity of setting up native runtimes, model loading, and inference pipelines, so developers can integrate local AI into their apps through a simple API.
A repository dedicated to finetuning phi2 models using advanced machine learning techniques. This includes training scripts, model evaluation methods, and data processing tools.
A text-to-audio application that turns words and sentiments into melodies.
A project for fine-tuning large language models (LLMs) on curated Wikipedia datasets, featuring data preprocessing, model training with Phi-2, and evaluation using Python and Jupyter notebooks.
Fine-tuning Phi-2 with LoRA for grid-based spatial reasoning and Chain-of-Thought (CoT) inference.
This project evaluates and compares two LLMs on various software engineering tasks, including code generation, test generation, and documentation. The models used are phi-2 and Cohere Command.
Locally fine-tuned, memory-aware AI assistant built with Phi-2 + QLoRA, Qdrant, DuckDuckGo, and Gradio. Your own private ChatGPT.
A QLoRa approach to teach Persian reasoning to Phi2-microsoft
Use of phi2 for custom use.
AI Comedian
🧬 A Study & Web Application Exploring the Capabilities of LLMs in Cross-Language Code Translation
Fine-tuning Microsoft’s Phi-2 model using QLoRA on the Alpaca-cleaned dataset with 4-bit quantization (bitsandbytes).
Using the Group News 20 dataset, this project employs the microsoft/phi1.5 model for text classification via prompting. We redefine classification as label generation, evaluating results with standard metrics and uploading the model on HuggingFace
No description provided.
LLM Prompt Recovery
Projekt u sklopu predmeta Obrada prirodnog jezika
🧬 A Comparative Study of LLMs and Code Translation