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Open Source Model Profile · DeepMount00

Lexora-Medium-7B

Lexora-Medium-7B is a 7.62B-parameter Qwen2 text-generation model from DeepMount00. Its model card documents Transformers loading and an Italian chat-template example.

Publisher
DeepMount00
Task
text-generation
Model type
qwen2
License
apache-2.0
Library
transformers
Publication status
Accepted · not indexed

Model overview

Lexora-Medium-7B is published by DeepMount00 as a conversational text-generation model. The captured configuration identifies Qwen2ForCausalLM with a qwen2 model type, and Safetensors metadata reports 7615616512 parameters. Hub tags reference Italian and English with Sonnet-3.5-ITA dataset names, while the card shows a Transformers loading snippet.

Recorded capabilities

7.62B Qwen2 scale

Captured config identifies Qwen2ForCausalLM and Safetensors metadata reports 7615616512 parameters.

Transformers loading snippet

According to the model card, loading uses AutoTokenizer and AutoModelForCausalLM with bfloat16 and automatic device mapping.

Italian chat example

The model card shows an Italian arithmetic prompt processed with apply_chat_template and generation up to 1024 new tokens.

Use cases in the source record

  • Conversational text generation consistent with the captured conversational and text-generation tags.
  • Transformers chat-template experiments following the card's Italian prompt and generation example.

Limitations and unknowns

  • 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.
  • Dataset associations come from hub tags and a brief code-only card and have not been independently verified by Ethen.

Source and provenance

Source: DeepMount00/Lexora-Medium-7B

Captured: Unknown. Processed: 2026-09-07T19:34:30.185586+00:00.

How to Use import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "DeepMount00/Lexora-Medium-7B" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map= "auto" , ) prompt = [{ 'role' : 'user' , 'content' : """Marco ha comprato 5 scatole di cioccolatini. Ogni scatola contiene 12 cioccolatini. Ha deciso di dare 3 cioccolatini a ciascuno dei suoi 7 amici. Quanti cioccolatini gli rimarranno dopo averli distribuiti ai suoi amici?""" }] inputs = tokenizer.apply_chat_template( prompt, add_generation_prompt= True ,…

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