Tagged fine-tuning

Fine-tune large language models with LoRA on limited GPU memory

Fine-tune language models with SFT, DPO, GRPO and reward modeling in TRL

Fine-tune models with LoRA or QLoRA faster and with less GPU memory using Unsloth

Train a custom LoRA image model on fal.ai for a brand, character or style

Manage Mistral AI files, fine-tuning jobs, batch runs and OCR through Composio

Turn bad agent cases into DPO preference pairs for LoRA fine-tuning

Pick the right post-training route for an agent model, from SFT to RL

Load Hugging Face models, run pipelines and fine-tune with the Transformers Trainer

Embed microbiome samples and fine-tune Waypoint models with the waypoint CLI

Format, validate and document datasets for fine-tuning with chat templates

Build the eval harness, golden sets and graders that gate every fine-tuning run

Decide whether to fine-tune and choose SFT, DPO, GRPO or continued pretraining

Configure LoRA and QLoRA fine-tuning with rank, alpha and target module choices

Export fine-tuned models as merged safetensors, LoRA, GGUF or FP8 for deployment

Turn eval traces and production logs into SFT examples and preference pairs

Fine-tune vision-language models on image and text data with LoRA

Fine-tune a model with GRPO on managed GPUs from a task and a dataset

Fine-tune language models with SFT, DPO and GRPO using TRL on cloud GPUs

Run LLM systems with RAG, embeddings, fine-tuning and quality evals

Choose API, fine-tune or in-house models and classify AI risk under the EU AI Act

Fine-tune open and Gemini models on Agent Platform infrastructure

Build a book-to-SFT pipeline with ePub extraction, segmentation and LoRA training

Fine-tune LLMs with LoRA and QLoRA, including dataset prep and tuning

Run GPU training, fine-tuning and inference on Modal with no SSH or Docker setup

Apply language model pretraining and fine-tuning practices to training code

Fine-tune 100+ LLMs with Axolotl YAML configs, LoRA and preference methods

Fine-tune OpenVLA-OFT robot policies with LoRA and evaluate them on LIBERO

Fine-tune and serve OpenPI robot policy models with JAX or PyTorch backends

Fine-tune LLMs with TRL using SFT, DPO, PPO, GRPO and reward models

Fine-tune reasoning models with GRPO reinforcement learning in TRL

Implement, train and fine-tune Llama, Gemma, Phi and Qwen models with LitGPT

Fine-tune 100+ LLMs with LLaMA-Factory WebUI, QLoRA and multimodal support

Fine-tune LLMs with LoRA or QLoRA adapters and JSONL datasets using Hugging Face PEFT

Pick the right fine-tuning framework for MiniCPM5 SFT, LoRA or DPO training

Train a LoRA adapter for MiniCPM5-1B and convert it to GGUF for llama.cpp

Fine-tune MiniCPM5 with LLaMA-Factory YAML configs, SFT, DPO and WebUI

Fine-tune MiniCPM5 with ms-swift and Megatron-SWIFT using the right template

Fine-tune MiniCPM5 with bare TRL and PEFT, including assistant-only loss

Run low-VRAM LoRA and QLoRA fine-tuning of MiniCPM5 on one consumer GPU with Unsloth

Fine-tune MiniCPM5-1B with xtuner using config-file-driven training

Fine-tune language and vision models with TRL or Unsloth on cloud GPUs

Build and publish a Gradio demo on Hugging Face Spaces for a LoRA adapter

Fine-tune sentence embedding and reranker models for retrieval and similarity

Post-train LLMs with SFT, DPO, GRPO, KTO and reward models using the TRL library

Hunt LLM training data and model poisoning in fine-tuning and feedback loops

Fine-tune LLMs on Google Colab GPUs with Unsloth via a remote bridge

Run open model inference and fine-tuning on Fireworks with a chat API

Fine-tune LLMs with supervised, RL and LoRA training on Tinker cloud

Estimate Tinker fine-tuning costs from dataset token counts