Gensyn base linkage
Hub tags name Gensyn/Qwen2.5-0.5B-Instruct as the base model and fine-tune source. According to the model card, this record is its fine-tuned version.
Open Source Model Profile · 68g34eg
This 68g34eg model is a 494M-parameter Qwen2 text-generation fine-tune. According to the model card, it is a fine-tuned version of Gensyn/Qwen2.5-0.5B-Instruct.
The model is published by 68g34eg as a Qwen2 text-generation fine-tune. The captured configuration identifies Qwen2ForCausalLM with a qwen2 model type, and Safetensors metadata reports 494,032,768 parameters. According to the model card, it is a fine-tuned version of Gensyn/Qwen2.5-0.5B-Instruct trained using TRL.
Hub tags name Gensyn/Qwen2.5-0.5B-Instruct as the base model and fine-tune source. According to the model card, this record is its fine-tuned version.
Hub tags include trl, grpo, rl-swarm, and gensyn. According to the model card, the model was trained using TRL with GRPO from the DeepSeekMath reasoning work.
According to the model card, a transformers text-generation pipeline example runs the model on a user turn with up to 128 new tokens.
Source: 68g34eg/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-dense_carnivorous_caterpillar
Captured: Unknown. Processed: 2026-09-07T19:35:02.088740+00:00.
Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-dense_carnivorous_caterpillar This model is a fine-tuned version of Gensyn/Qwen2.5-0.5B-Instruct . It has been trained using TRL . Quick start 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= "68g34eg/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-dense_carnivorous_caterpillar" , device= "cuda" ) output = generator([{ "role" : "user" , "content" : question}], max_new_tokens= 128 , return_full_text= False )[ 0 ] print (output[…
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