7.62B Qwen2 scale
Captured config identifies Qwen2ForCausalLM and Safetensors metadata reports 7615616512 parameters.
Open Source Model Profile · sorgfresser
testtrainsft
Testtrainsft is a 7.62B-parameter Qwen2 text-generation fine-tune from sorgfresser. According to the model card, it fine-tunes AI-MO/Kimina-Autoformalizer-7B using TRL and SFT.
- Publisher
- sorgfresser
- Task
- text-generation
- Model type
- qwen2
- License
- Unknown
- Library
- transformers
- Publication status
- Accepted · not indexed
Model overview
Testtrainsft is published by sorgfresser as a conversational text-generation model. The captured configuration identifies Qwen2ForCausalLM with a qwen2 model type, and Safetensors metadata reports 7615616512 parameters. The model card describes it as a fine-tuned version of AI-MO/Kimina-Autoformalizer-7B, consistent with hub base-model tags.
Recorded capabilities
Kimina base
According to the model card, the starting point is AI-MO/Kimina-Autoformalizer-7B.
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: sorgfresser/testtrainsft
Captured: Unknown. Processed: 2026-09-07T19:36:02.099213+00:00.
Model Card for testtrainsft This model is a fine-tuned version of AI-MO/Kimina-Autoformalizer-7B . 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= "sorgfresser/testtrainsft" , device= "cuda" ) output = generator([{ "role" : "user" , "content" : question}], max_new_tokens= 128 , return_full_text= False )[ 0 ] print (output[ "generated_text" ]) Training procedure This model was trained with SFT. Framework versions TRL: 0…
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