openai/gsm8k
Benchmark • Updated • 17.6k • 931k • 1.38k
How to use NamrataThakur/llama31-8bn_SFT with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("question-answering", model="NamrataThakur/llama31-8bn_SFT") # Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM
tokenizer = AutoTokenizer.from_pretrained("NamrataThakur/llama31-8bn_SFT")
model = AutoModelForMultimodalLM.from_pretrained("NamrataThakur/llama31-8bn_SFT")How to use NamrataThakur/llama31-8bn_SFT 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 NamrataThakur/llama31-8bn_SFT 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 NamrataThakur/llama31-8bn_SFT to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for NamrataThakur/llama31-8bn_SFT to start chatting
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="NamrataThakur/llama31-8bn_SFT",
max_seq_length=2048,
)This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
| Base Model | Fine-Tuning | Train Dataset | Validation Loss | Evaluation Dataset | Mean Answer Relevancy Score | Mean Answer Correctness Score |
|---|---|---|---|---|---|---|
| Llama3.1-8bn | Supervised Fine-Tuning | GSM8K | 1.12 | SmallThoughts | 0.736 | 0.437 |