About 494.0M parameters
Safetensors metadata reports 494,032,768 parameters, or about 494.0M.
Open Source Model Profile · Ertman
Qwen2.5-0.5B-Instruct-Gensyn-Swarm-iridescent_tropical_starfish is a 494.0M-parameter Qwen2 text-generation model from Ertman. The model card describes it as a fine-tuned version of unsloth/Qwen2.5-0.5B-Instruct trained using TRL.
Ertman publishes Qwen2.5-0.5B-Instruct-Gensyn-Swarm-iridescent_tropical_starfish as a text-generation model. Captured config identifies Qwen2ForCausalLM with a qwen2 model type, and Safetensors metadata reports 494,032,768 parameters. Hub tags include generated_from_trainer, rl-swarm, grpo, gensyn, trl, genrl-swarm, conversational, and unsloth/Qwen2.5-0.5B-Instruct as base model and finetune. No license value was extracted.
Safetensors metadata reports 494,032,768 parameters, or about 494.0M.
The model card says this is a fine-tuned version of unsloth/Qwen2.5-0.5B-Instruct trained using TRL.
The card says training used GRPO, a method introduced in DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.
Source: Ertman/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-iridescent_tropical_starfish
Captured: Unknown. Processed: 2026-09-07T19:35:05.118661+00:00.
Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-iridescent_tropical_starfish This model is a fine-tuned version of unsloth/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= "Ertman/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-iridescent_tropical_starfish" , device= "cuda" ) output = generator([{ "role" : "user" , "content" : question}], max_new_tokens= 128 , return_full_text= False )[ 0 ] print (output[ "…
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