Hahmdong/SPUPER-qwen2.5-7b-acaciawl-add-2k

TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:7.6BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Aug 18, 2026Architecture:Transformer Featherless Exclusive Cold

Hahmdong/SPUPER-qwen2.5-7b-acaciawl-add-2k is a 7.6 billion parameter language model, fine-tuned from Qwen/Qwen2.5-7B-Instruct using the TRL framework. This model is designed for general text generation tasks, leveraging its instruction-tuned base for diverse conversational and creative applications. It offers a substantial 32K context window, making it suitable for processing longer inputs and generating coherent, extended responses. The fine-tuning process aims to enhance its performance on instruction-following tasks.

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Model Overview

Hahmdong/SPUPER-qwen2.5-7b-acaciawl-add-2k is a 7.6 billion parameter language model, fine-tuned from the robust Qwen/Qwen2.5-7B-Instruct base model. This fine-tuning was performed using the TRL (Transformer Reinforcement Learning) framework, specifically employing a Supervised Fine-Tuning (SFT) approach.

Key Capabilities

  • Instruction Following: Inherits and enhances the instruction-following capabilities of its Qwen2.5-7B-Instruct base.
  • Text Generation: Capable of generating coherent and contextually relevant text for a variety of prompts.
  • Extended Context: Benefits from a 32,768 token context window, allowing for processing and generating longer sequences.

Training Details

The model was trained using Supervised Fine-Tuning (SFT) with the TRL library. The training process and metrics can be visualized via Weights & Biases, as linked in the original model card. The development environment utilized specific versions of key frameworks:

  • TRL: 0.27.1
  • Transformers: 4.57.6
  • Pytorch: 2.9.0
  • Datasets: 4.0.0
  • Tokenizers: 0.22.2

Use Cases

This model is suitable for applications requiring general-purpose text generation and instruction-based interactions, such as:

  • Conversational AI: Engaging in dialogue and answering questions based on provided instructions.
  • Content Creation: Generating creative text, summaries, or expanded responses from prompts.
  • Prototyping: Quickly developing applications that leverage a capable instruction-tuned language model.

Finetunes
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