Instructions to use Nicohst/Llama-3-Clembench-Runs-Successful-Episodes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Nicohst/Llama-3-Clembench-Runs-Successful-Episodes with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/meta-llama-3.1-8b-instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Nicohst/Llama-3-Clembench-Runs-Successful-Episodes") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use Nicohst/Llama-3-Clembench-Runs-Successful-Episodes with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Nicohst/Llama-3-Clembench-Runs-Successful-Episodes to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Nicohst/Llama-3-Clembench-Runs-Successful-Episodes to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Nicohst/Llama-3-Clembench-Runs-Successful-Episodes to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Nicohst/Llama-3-Clembench-Runs-Successful-Episodes", max_seq_length=2048, )
| base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit | |
| library_name: peft | |
| license: llama3.1 | |
| tags: | |
| - trl | |
| - sft | |
| - unsloth | |
| - generated_from_trainer | |
| model-index: | |
| - name: Llama-3-Clembench-Runs-Successful-Episodes | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/nicola-er-ho/clembench-playpen-sft/runs/50ff4eak) | |
| # Llama-3-Clembench-Runs-Successful-Episodes | |
| This model is a fine-tuned version of [unsloth/meta-llama-3.1-8b-instruct-bnb-4bit](https://huggingface.co/unsloth/meta-llama-3.1-8b-instruct-bnb-4bit) on the None dataset. | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 0.0002 | |
| - train_batch_size: 4 | |
| - eval_batch_size: 8 | |
| - seed: 7331 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_ratio: 0.03 | |
| - lr_scheduler_warmup_steps: 5 | |
| - num_epochs: 1 | |
| ### Training results | |
| ### Framework versions | |
| - PEFT 0.12.0 | |
| - Transformers 4.44.2 | |
| - Pytorch 2.4.0+cu121 | |
| - Datasets 2.21.0 | |
| - Tokenizers 0.19.1 |