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FlameF0X
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Model Details
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FlameF0X
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Input Modalities
Output Modalities
Supported Functionality
This model was trained on a mixture of datasets to balance narrative capability and factual grounding:
You can load this model using the transformers library:
python
from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "FlameF0X/TinyMoE-100M-A1K"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id) input_text = "Once upon a time,"inputs = tokenizer(input_text, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=50)print(tokenizer.decode(outputs[0], skip_special_tokens=True))This model is intended for research and edge applications where low latency and small memory footprints are required. Due to its MoE nature, it maintains the parameter count of a larger model while keeping the inference speed of a much smaller dense model.
The model was trained with the following core configurations:
from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "FlameF0X/TinyMoE-100M-A1K"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id) input_text = "Once upon a time,"inputs = tokenizer(input_text, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=50)print(tokenizer.decode(outputs[0], skip_special_tokens=True))