1.54B Qwen2 scale
Captured config identifies Qwen2ForCausalLM and Safetensors metadata reports 1543714304 parameters.
Open Source Model Profile · T1anyu
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 T1anyu. According to the model card, it fine-tunes Qwen2.5-1.5B-Instruct on a math dataset using TRL and SFT.
- Publisher
- T1anyu
- 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 T1anyu as a conversational text-generation model. The captured configuration identifies Qwen2ForCausalLM with a qwen2 model type, and Safetensors metadata reports 1543714304 parameters. The model card describes it as a fine-tuned version of Qwen/Qwen2.5-1.5B-Instruct on open-r1/OpenR1-Math-220k, consistent with hub base-model and dataset tags.
Recorded capabilities
Math-dataset fine-tune
According to the model card, the model fine-tunes Qwen/Qwen2.5-1.5B-Instruct on open-r1/OpenR1-Math-220k.
TRL and SFT workflow
The model card states training used TRL and describes the training procedure as SFT.
Use cases in the source record
- Conversational text generation consistent with the captured conversational and text-generation tags.
- Transformers pipeline trials following the card's text-generation quick-start example.
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: T1anyu/Qwen2.5-1.5B-Open-R1-Distill
Captured: Unknown. Processed: 2026-09-07T19:35:16.187114+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= "T1anyu/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 proced…
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