SFT Final Models
Collection
Models that were trained on clembench v0.9 - v1.6 • 4 items • Updated
How to use clembench-playpen/llama-3.1-8B-Instruct_playpen_SFT_DFINAL_0.7K-steps 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, "clembench-playpen/llama-3.1-8B-Instruct_playpen_SFT_DFINAL_0.7K-steps")How to use clembench-playpen/llama-3.1-8B-Instruct_playpen_SFT_DFINAL_0.7K-steps with Unsloth Studio:
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-3.1-8B-Instruct_playpen_SFT_DFINAL_0.7K-steps to start chatting
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-3.1-8B-Instruct_playpen_SFT_DFINAL_0.7K-steps to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for clembench-playpen/llama-3.1-8B-Instruct_playpen_SFT_DFINAL_0.7K-steps to start chatting
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="clembench-playpen/llama-3.1-8B-Instruct_playpen_SFT_DFINAL_0.7K-steps",
max_seq_length=2048,
)This model is a fine-tuned version of unsloth/meta-llama-3.1-8b-instruct-bnb-4bit on the None dataset. It achieves the following results on the evaluation set:
More information needed
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.2589 | 0.0565 | 100 | 0.3587 |
| 0.1837 | 0.1130 | 200 | 0.2943 |
| 0.1982 | 0.1695 | 300 | 0.2688 |
| 0.158 | 0.2260 | 400 | 0.2513 |
| 0.1527 | 0.2825 | 500 | 0.2402 |
| 0.147 | 0.3390 | 600 | 0.2392 |
| 0.1022 | 0.3955 | 700 | 0.2372 |