LEMON07R/GEMMA-2-ATARAXY-V4C-9B_FIXED

Introduction

The LEMON07R/GEMMA-2-ATARAXY-V4C-9B_FIXED model is a text generation model built by merging pre-trained language models using the mergekit tool. It is designed to effectively perform various text generation tasks by leveraging the strengths of its constituent models. This model has been evaluated across several benchmarks and provides competitive performance.

Architecture

The model is created using the SLERP merge method, combining the following models:

lemon07r/Gemma-2-Ataraxy-v3b-9B
zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25

The SLERP method was applied with a specific configuration that includes using bfloat16 as the data type and a parameter t set to 0.25. The architecture involves slicing the models based on layer ranges to integrate them seamlessly.

Training

This model does not undergo traditional training since it is a merge of pre-trained models. Instead, it optimizes performance by trimming excess weights (lm_head.weight) from the merged model.

Guide: Running Locally

Install Dependencies: Ensure you have Python and the Hugging Face Transformers library installed.
```
pip install transformers
```

Load the Model: Use the Transformers library to load the model.

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("lemon07r/Gemma-2-Ataraxy-v4c-9B")
model = AutoModelForCausalLM.from_pretrained("lemon07r/Gemma-2-Ataraxy-v4c-9B")

Run Inference: Generate text with the model.

input_text = "Once upon a time"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
output = model.generate(input_ids)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Suggested Cloud GPUs

AWS EC2: P3 instances
Google Cloud: A2 instances
Azure: NCv3-series

These cloud services provide powerful GPUs that can efficiently handle model inference tasks.

License

The specific licensing terms for the LEMON07R/GEMMA-2-ATARAXY-V4C-9B_FIXED model are not detailed in the provided information. Users should refer to the model page on Hugging Face for any license-related information before using the model.

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