Xclbr7/caliburn-12b

caliburn 12b-merged

This model is a 12 billion parameter language model created by merging multiple existing models using the MergeKit library. It is designed for general text generation tasks.

Model Details

Model Description

This is a large language model with 12 billion parameters, created by merging multiple pre-existing models using the MergeKit library. The model is based on the transformer architecture and is fine-tuned for general text generation tasks.

• Developed by: The user who created this merged model
• Model type: Transformer-based language model
• Language(s) (NLP): English
• License: MIT
• Finetuned from model: Multiple source models merged using MergeKit

Model Sources [optional]

• Repository: [More Information Needed]
• Paper [optional]: N/A
• Demo [optional]: N/A

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	22.68
IFEval (0-Shot)	35.76
BBH (3-Shot)	35.64
MATH Lvl 5 (4-Shot)	9.67
GPQA (0-shot)	11.52
MuSR (0-shot)	13.78
MMLU-PRO (5-shot)	29.72

Direct Use

This model can be used for various natural language processing tasks, including:

• Text generation
• Code completion
• Question answering
• Summarization

Downstream Use [optional]

The model can be fine-tuned for specific tasks or domains to improve performance on targeted applications.

Out-of-Scope Use

This model should not be used for generating harmful, biased, or unethical content. It should not be relied upon for critical decision-making without human oversight.

Bias, Risks, and Limitations

• The model may inherit biases present in its training data or source models.
• It may generate incorrect or nonsensical information.
• The model's outputs should be carefully reviewed and fact-checked.

Recommendations

Users should be aware of the model's limitations and potential biases. It's recommended to use the model with appropriate content filtering and human oversight, especially for public-facing applications.

How to Get Started with the Model

Use the following code to get started with the model:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

tokenizer = AutoTokenizer.from_pretrained("./models/12b-merged")
model = AutoModelForCausalLM.from_pretrained("./models/12b-merged", torch_dtype=torch.float16).to("cuda")

prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs.to("cuda"), max_new_tokens=100)
result = tokenizer.batch_decode(outputs, skip_special_tokens=True)
print(result)