1. Model Details
Introducing xinchen9/llama3-b8-ft, an advanced language model comprising 8 billion parameters. It has been fine-trained based on Meta-Llama-3-8.
There are two steps: 1:The llama3-b8 model was fine-tuning on dataset SlimOrca. 2: With CoT distillation.
2. How to Use
Here give some examples of how to use our model.
Text Completion
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_name = "deepseek-ai/deepseek-llm-7b-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id
3 Disclaimer
The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 13.85 |
IFEval (0-Shot) | 15.46 |
BBH (3-Shot) | 24.73 |
MATH Lvl 5 (4-Shot) | 3.17 |
GPQA (0-shot) | 8.39 |
MuSR (0-shot) | 6.41 |
MMLU-PRO (5-shot) | 24.93 |
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Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard15.460
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard24.730
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard3.170
- acc_norm on GPQA (0-shot)Open LLM Leaderboard8.390
- acc_norm on MuSR (0-shot)Open LLM Leaderboard6.410
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard24.930