metadata
language:
- en
license: apache-2.0
library_name: transformers
tags:
- pytorch
- text-generation
- custom-model
pipeline_tag: token-classification
inference: true
base_model:
- Qwen/Qwen2.5-0.5B
Model Card for zeltera/mcma
Model Description
zeltera/mcma is a machine learning model hosted on the Hugging Face Hub. Based on the file structure in the repository, this appears to be a Transformers-compatible model (PyTorch/Safetensors).
- Developed by: Zeltera
- Model type: Pre-trained / Fine-tuned Transformer
- Language(s): English
- License: Apache 2.0 (or specify your license)
- Repository: zeltera/mcma
Intended Uses & Limitations
Intended Use
This model is designed for tasks such as:
- Text generation
- Feature extraction
- (Update this list based on the specific capabilities of your model)
Limitations
- The model may output biased or inaccurate information.
- Performance depends on the quality of the input prompts.
How to Use
You can use this model directly with the Hugging Face transformers library.
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load model and tokenizer
model_name = "zeltera/mcma"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
input_text = "Once upon a time"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))