OpenR1-Math fine-tune lineage
According to the model card, this is a fine-tuned version of Qwen/Qwen2.5-1.5B-Instruct on the open-r1/OpenR1-Math-220k dataset.
Open Source Model Profile · Monika2025
Qwen2.5-1.5B-Open-R1-Distill
Qwen2.5-1.5B-Open-R1-Distill is a 1.54B-parameter Qwen2 text-generation fine-tune from Monika2025. According to the model card, it fine-tunes Qwen/Qwen2.5-1.5B-Instruct on the open-r1/OpenR1-Math-220k dataset using TRL and SFT.
- Publisher
- Monika2025
- Task
- text-generation
- Model type
- qwen2
- License
- Unknown
- Library
- transformers
- Publication status
- Accepted · not indexed
Model overview
Qwen2.5-1.5B-Open-R1-Distill is published by Monika2025 as a Qwen2 text-generation model. The captured configuration identifies Qwen2ForCausalLM and Safetensors metadata reports 1,543,714,304 parameters. According to the model card, it is a fine-tuned version of Qwen/Qwen2.5-1.5B-Instruct on the open-r1/OpenR1-Math-220k dataset, trained with SFT using TRL.
Recorded capabilities
TRL and SFT training
According to the model card, the model was trained with SFT using TRL.
Qwen2 Transformers record
Captured configuration records Qwen2ForCausalLM with model type qwen2 and Transformers library support.
Use cases in the source record
- Math-oriented text-generation experiments building on the publisher-described OpenR1-Math-220k fine-tune, run through the documented Transformers pipeline workflow.
Limitations and unknowns
- No license value was extracted from this record.
- No evaluation results were extracted from this record.
- No context-window value was extracted from this record.
- Provider state is historical snapshot data, not independently refreshed current availability.
Source and provenance
Source: Monika2025/Qwen2.5-1.5B-Open-R1-Distill
Captured: Unknown. Processed: 2026-09-07T19:35:13.595953+00:00.
Model Card for Qwen2.5-1.5B-Open-R1-Distill This model is a fine-tuned version of Qwen/Qwen2.5-1.5B-Instruct on the open-r1/OpenR1-Math-220k dataset. 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= "Monika2025/Qwen2.5-1.5B-Open-R1-Distill" , device= "cuda" ) output = generator([{ "role" : "user" , "content" : question}], max_new_tokens= 128 , return_full_text= False )[ 0 ] print (output[ "generated_text" ]) Training pr…
F001F002F003F004F005F006F007F008F009F010F011F012F013