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FlameF0X
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Model Details
Model Provider
FlameF0X
Model Tree
FlameF0X/TinyMoE-100m-2x8
Input Modalities
Output Modalities
Supported Functionality
python
from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"generator = pipeline("text-generation", model="FlameF0X/TinyMoE-100m-2x8-ultrachat", device="cuda")output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]print(output["generated_text"])This model was trained with SFT.
Cite TRL as:
bibtex
@software{vonwerra2020trl, title = {{TRL: Transformers Reinforcement Learning}}, author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin}, license = {Apache-2.0}, url = {https://github.com/huggingface/trl}, year = {2020}}This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.
python
from transformers import AutoModelForCausalLM, AutoTokenizer model_id = 'FlameF0X/TinyMoE-100m-2x8-ultrachat'tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id)For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.
from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"generator = pipeline("text-generation", model="FlameF0X/TinyMoE-100m-2x8-ultrachat", device="cuda")output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]print(output["generated_text"])@software{vonwerra2020trl, title = {{TRL: Transformers Reinforcement Learning}}, author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin}, license = {Apache-2.0}, url = {https://github.com/huggingface/trl}, year = {2020}}