Brigitte Tousignant

BrigitteTousi

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liked a Space 2 days ago
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reacted to ginipick's post with ๐Ÿ”ฅ 3 days ago
๐ŸŒ GraphMind: Phi-3 Instruct Graph Explorer โœจ Extract and visualize knowledge graphs from any text in multiple languages! GraphMind is a powerful tool that leverages the capabilities of Phi-3 to transform unstructured text into structured knowledge graphs, helping you understand complex relationships within any content. https://huggingface.co/spaces/ginigen/Graph-Mind ๐Ÿš€ Key Features Multi-language Support ๐ŸŒ: Process text in English, Korean, and many other languages Instant Visualization ๐Ÿงฉ: See extracted entities and relationships in an interactive graph Entity Recognition ๐Ÿท๏ธ: Automatically identifies and categorizes named entities Optimized Performance โšก: Uses caching to deliver faster results for common examples Intuitive Interface ๐Ÿ‘†: Simple design makes complex graph extraction accessible to everyone ๐Ÿ’ก Use Cases Content Analysis: Extract key entities and relationships from articles or documents Research Assistance: Quickly visualize connections between concepts in research papers Educational Tool: Help students understand the structure of complex texts Multilingual Processing: Extract knowledge from content in various languages ๐Ÿ”ง How It Works Enter any text in the input field Select a model from the dropdown Click "Extract & Visualize" Explore the interactive knowledge graph and entity recognition results GraphMind bridges the gap between raw text and structured knowledge, making it easier to identify patterns, extract insights, and understand relationships within any content. Try it now and transform how you interact with textual information! #NLP #KnowledgeGraph #TextAnalysis #Visualization #Phi3 #MultilingualAI
replied to burtenshaw's post 3 days ago
everybody and their dog is fine-tuning Gemma 3 today, so I thought I'd do a longer post on the tips and sharp edges I find. let's go! 1. has to be install everything form main and nightly. this is what I'm working with to get unsloth and TRL running ```txt git+https://github.com/huggingface/transformers@main git+https://github.com/huggingface/trl.git@main bitsandbytes peft ``` plus this with `--no-deps` ```txt git+https://github.com/unslothai/unsloth-zoo.git@nightly git+https://github.com/unslothai/unsloth.git@nightly ``` 2. will brown's code to turn GSM8k into a reasoning dataset is a nice toy experiment https://gist.github.com/willccbb/4676755236bb08cab5f4e54a0475d6fb 3. with a learning rate of 5e-6 rewards and loss stayed flat for the first 100 or so steps. 4. so far none of my runs have undermined the outputs after 1 epoch. therefore, I'm mainly experimenting with bigger LoRA adapters. ```python from trl import GRPOConfig training_args = GRPOConfig( learning_rate = 5e-6, adam_beta1 = 0.9, adam_beta2 = 0.99, weight_decay = 0.1, warmup_ratio = 0.1, lr_scheduler_type = "cosine", optim = "adamw_8bit", logging_steps = 1, per_device_train_batch_size = 2, gradient_accumulation_steps = 1, num_generations = 2, max_prompt_length = 256, max_completion_length = 1024 - 256, num_train_epochs = 1, max_steps = 250, save_steps = 250, max_grad_norm = 0.1, report_to = "none", ) ``` 5. vision fine-tuning isn't available in TRL's GRPOTrainer, so stick to text datasets. but no need to load the model differently in transformers or Unsloth ```python from transformers import AutoModelForImageTextToText model = AutoModelForImageTextToText.from_pretrained("google/gemma-3-4b-it) ``` if you want an introduction to GRPO, check out the reasoning course, it walks you through the algorithm, theory, and implementation in a smooth way. https://huggingface.co/reasoning-course
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BrigitteTousi's activity

New activity in sasha/pedro-pascal almost 2 years ago

oh my goodness

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#1 opened almost 2 years ago by
EmilyWitko
New activity in huggingface/README over 2 years ago