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Kailune-AI/mamba-gpt-7b-v2
mamba-gpt-7b-v2 is a text generation model from Kailune-AI. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
We have fine-tuned the OpenLLaMA model and surpassed the original model in multiple evaluation subtasks, making it currently one of the best performing 3B model, with comparable performance to llama-7b. - Base model:…
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From the Hugging Face model README
We have fine-tuned the OpenLLaMA model and surpassed the original model in multiple evaluation subtasks, making it currently one of the best performing 3B model, with comparable performance to llama-7b.
To use the model with the transformers library on a machine with GPU(s), first make sure you have the transformers, accelerate and torch libraries installed.
Ensure you are utilizing a stable version of Transformers, 4.34.0 or newer.
Then, run the following Python snippet:
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("CobraMamba/mamba-gpt-7b-v2")
model = AutoModelForCausalLM.from_pretrained("CobraMamba/mamba-gpt-7b-v2", trust_remote_code=True, torch_dtype=torch.float16)
input_content = "Your text here"
input_ids = tokenizer.encode(input_content, return_tensors="pt")
output = model.generate(input_ids, max_length=128, temperature=0.7)
output_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(output_text)
If this work is helpful, please kindly cite as:
@Misc{mamba-gpt-7b-v2,
title = {Mamba-GPT-7b-v2},
author = {chiliu},
howpublished = {\url{https://huggingface.co/CobraMamba/mamba-gpt-7b-v2}},
year = {2023}
}
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 54.85 |
| ARC (25-shot) | 61.95 |
| HellaSwag (10-shot) | 83.83 |
| MMLU (5-shot) | 61.74 |
| TruthfulQA (0-shot) | 46.63 |
| Winogrande (5-shot) | 78.45 |
| GSM8K (5-shot) | 17.29 |
| DROP (3-shot) | 34.07 |