Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slow_tangled_seahorse

Model overview

Mursik358 publishes Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slow_tangled_seahorse 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, conversational, and Gensyn/Qwen2.5-0.5B-Instruct as base model and finetune. No license value was extracted.

Recorded capabilities

Use cases in the source record

• Transformers text-generation and chat-style pipeline experiments following the card's example.

Limitations and unknowns

• No evaluation scores were extracted from this record.
• No license value was extracted from this record.
• The slow_tangled_seahorse name token and the hub tag I am slow tangled seahorse are not treated as independently verified identity or capability claims.
• Provider state is historical snapshot data, not independently refreshed current availability.
• The Gensyn/Qwen2.5-0.5B-Instruct fine-tune and TRL training claims come from the publisher model card and have not been independently verified by Ethen.

Source and provenance

Source: Mursik358/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slow_tangled_seahorse

Captured: Unknown. Processed: 2026-09-07T19:35:13.902003+00:00.

Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slow_tangled_seahorse 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= "Mursik358/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slow_tangled_seahorse" , device= "cuda" ) output = generator([{ "role" : "user" , "content" : question}], max_new_tokens= 128 , return_full_text= False )[ 0 ] print (output[ "generated_te…