laion/exp-uns-r2egym-2_1x_glm_4_7_traces_jupiter_cleaned
This 8 billion parameter model is a fine-tuned version of Qwen/Qwen3-8B, developed by laion, featuring a 32768 token context length. It was specifically trained on the /data/cat/ws/befe330h-befe330h-otagent/huggingface/hub/datasets--DCAgent--exp-uns-r2egym-2_1x_glm_4.7_traces_jupiter_cleaned dataset. The model is optimized for tasks related to the specific dataset it was fine-tuned on, suggesting a specialized application rather than general-purpose use.
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Model Overview
This model, exp-uns-r2egym-2_1x_glm_4_7_traces_jupiter_cleaned, is an 8 billion parameter language model fine-tuned from the Qwen/Qwen3-8B architecture. It was developed by laion and utilizes a substantial context length of 32768 tokens, indicating its capability to process and generate longer sequences of text.
Training Details
The model underwent fine-tuning on a specific dataset: /data/cat/ws/befe330h-befe330h-otagent/huggingface/hub/datasets--DCAgent--exp-uns-r2egym-2_1x_glm_4.7_traces_jupiter_cleaned. Key training hyperparameters included a learning rate of 4e-05, a total training batch size of 16 (with a per-device batch size of 1 and 2 gradient accumulation steps), and 7 epochs. The optimizer used was AdamW with specific beta and epsilon values, and a cosine learning rate scheduler with a 0.1 warmup ratio.
Potential Use Cases
Given its fine-tuning on a specialized dataset, this model is likely best suited for:
- Specific domain tasks: Applications directly related to the content and structure of the
/data/cat/ws/befe330h-befe330h-otagent/huggingface/hub/datasets--DCAgent--exp-uns-r2egym-2_1x_glm_4.7_traces_jupiter_cleaneddataset. - Research and experimentation: Exploring the impact of fine-tuning on a particular dataset using the Qwen3-8B base.
Users should note that the README indicates that more information is needed regarding the model's description, intended uses, limitations, and training/evaluation data, suggesting that its general applicability might be limited without further context on the fine-tuning dataset.