Instructions to use clembench-playpen/llama-3.1-70B-Instruct_playpen_SFT_DFINAL_1.7K-steps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use clembench-playpen/llama-3.1-70B-Instruct_playpen_SFT_DFINAL_1.7K-steps with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-70B-Instruct") model = PeftModel.from_pretrained(base_model, "clembench-playpen/llama-3.1-70B-Instruct_playpen_SFT_DFINAL_1.7K-steps") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use clembench-playpen/llama-3.1-70B-Instruct_playpen_SFT_DFINAL_1.7K-steps 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 clembench-playpen/llama-3.1-70B-Instruct_playpen_SFT_DFINAL_1.7K-steps 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 clembench-playpen/llama-3.1-70B-Instruct_playpen_SFT_DFINAL_1.7K-steps to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for clembench-playpen/llama-3.1-70B-Instruct_playpen_SFT_DFINAL_1.7K-steps to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="clembench-playpen/llama-3.1-70B-Instruct_playpen_SFT_DFINAL_1.7K-steps", max_seq_length=2048, )
llama-3.1-70B-Instruct_playpen_SFT_DFINAL_1.7K-steps
This model is a fine-tuned version of meta-llama/Llama-3.1-70B-Instruct on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2164
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: Use adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.03
- lr_scheduler_warmup_steps: 5
- training_steps: 1700
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.196 | 0.0565 | 100 | 0.2851 |
| 0.1654 | 0.1130 | 200 | 0.2506 |
| 0.1664 | 0.1695 | 300 | 0.2495 |
| 0.1407 | 0.2260 | 400 | 0.2423 |
| 0.1439 | 0.2825 | 500 | 0.2317 |
| 0.1369 | 0.3390 | 600 | 0.2291 |
| 0.0935 | 0.3955 | 700 | 0.2228 |
| 0.1252 | 0.4520 | 800 | 0.2290 |
| 0.0886 | 0.5085 | 900 | 0.2165 |
| 0.1079 | 0.5650 | 1000 | 0.2232 |
| 0.126 | 0.6215 | 1100 | 0.2131 |
| 0.1203 | 0.6780 | 1200 | 0.2188 |
| 0.093 | 0.7345 | 1300 | 0.2140 |
| 0.0991 | 0.7910 | 1400 | 0.2151 |
| 0.077 | 0.8475 | 1500 | 0.2138 |
| 0.0813 | 0.9040 | 1600 | 0.2142 |
| 0.0866 | 0.9605 | 1700 | 0.2164 |
Framework versions
- PEFT 0.14.0
- Transformers 4.47.1
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.21.0
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Model tree for clembench-playpen/llama-3.1-70B-Instruct_playpen_SFT_DFINAL_1.7K-steps
Base model
meta-llama/Llama-3.1-70B Finetuned
meta-llama/Llama-3.1-70B-Instruct