megatron_v1
megatron_v1 is a Mixure of Experts (MoE) made of mistral models.
π» Usage
from transformers import AutoTokenizer
import transformers
import torch
model = "Eurdem/megatron_v1"
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": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 68.82 |
AI2 Reasoning Challenge (25-Shot) | 65.96 |
HellaSwag (10-Shot) | 84.80 |
MMLU (5-Shot) | 65.02 |
TruthfulQA (0-shot) | 60.32 |
Winogrande (5-shot) | 79.79 |
GSM8k (5-shot) | 57.01 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard65.960
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard84.800
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard65.020
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard60.320
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard79.790
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard57.010