megatron_2.1_MoE_2x7B
megatron_2.1_MoE_2x7B is a Mixure of Experts (MoE).
π» Usage
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Eurdem/megatron_2.1_MoE_2x7B"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Tell me about AI."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=1024, do_sample=True, temperature=0.7, top_k=1000, top_p=0.95)
print(outputs[0]["generated_text"])
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 76.64 |
AI2 Reasoning Challenge (25-Shot) | 72.95 |
HellaSwag (10-Shot) | 88.94 |
MMLU (5-Shot) | 64.56 |
TruthfulQA (0-shot) | 78.20 |
Winogrande (5-shot) | 84.53 |
GSM8k (5-shot) | 70.66 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard72.950
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard88.940
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard64.560
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard78.200
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard84.530
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard70.660