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Quazim0t0

https://discord.gg/eeDfVF4FNe

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AI & ML interests

the hunchback of huggingface 🔙 joined: 1-20-2025 🦥unsloth user 4️⃣ Phi User 🔨 ai hobbyist 📫 On Leaderboards Top 100-200

Recent Activity

reacted to burtenshaw's post with 🤗 1 day 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

posted an update 3 days ago

Thank you to the Open LLM Leaderboard's team for offering it to the community for as long as they did. I only recently joined HF, and it provided a lot of incentive and information to make better models. Always will remember getting to #112 :D Anyone have a solid way to test my models privately? Please let me know!

new activity 3 days ago

open-llm-leaderboard/open_llm_leaderboard:It's been a wild ride, folks :) (end of the Open LLM Leaderboard)

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Thank you to the Open LLM Leaderboard's team for offering it to the community for as long as they did. I only recently joined HF, and it provided a lot of incentive and information to make better models.

Always will remember getting to #112 :D

Anyone have a solid way to test my models privately? Please let me know!

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538

Update to the Imagine side-project.
Just uploaded the 16Bit & Q4

Samples: (Used a base Microsoft Phi4 model)
*You may experience bugs with either the model or the Open WebUI function*
Open WebUI function: https://openwebui.com/f/quaz93/imagine_phi
Quazim0t0/Imagine-v0.5-16bit - Haven't tested
Quazim0t0/ImagineTest-v0.5-GGUF - Tested (Pictures)

Dataset: Quazim0t0/Amanita-Imagine
Small Dataset of 500+ entries, still working on it here and there when I can.
Pictures use the Open Web UI function I provided.

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