| Axolotl |
A framework for fine-tuning language models |
| Gemma |
Google's latest implementation of the Big Language Model |
- finetune-gemma.ipynb - gemma-sft.py - Gemma_finetuning_notebook.ipynb |
Fine-tuning notebooks and scripts |
| LLama2 |
Meta's Open Source Large Language Model |
- generate_response_stream.py - Llama2_finetuning_notebook.ipynb - Llama_2_Fine_Tuning_using_QLora.ipynb |
Implementation and fine-tuning guidelines |
| Llama3 |
Upcoming Meta Large Language Modeling Experiments |
- Llama3_finetuning_notebook.ipynb |
Initial fine-tuning experiments |
| LlamaFactory |
A Framework for Training and Deployment of Large Language Models |
| LLMArchitecture/ParameterCount |
Technical details of the model architecture |
| Mistral-7b |
Mistral AI The 7 billion parameter model |
- LLM_evaluation_harness_for_Arc_Easy_and_SST.ipynb - Mistral_Colab_Finetune_ipynb_Colab_Final.ipynb - notebooks_chatml_inference.ipynb - notebooks_DPO_fine_tuning.ipynb - notebooks_SFTTrainer TRL.ipynb - SFT.py |
Integrated notebook for assessment, fine-tuning and reasoning |
| Mixtral |
Mixtral's Expert Mixing Model |
- Mixtral_fine_tuning.ipynb |
Fine-tuning Realization |
| VLM |
visual language model |
- Florence2_finetuning_notebook.ipynb - PaliGemma_finetuning_notebook.ipynb |
Visual language model implementation |
