didula-wso2/Qwen3-8B_julia_planning-ep2sft_16bit_vllm
The didula-wso2/Qwen3-8B_julia_planning-ep2sft_16bit_vllm is an 8 billion parameter Qwen3 model developed by didula-wso2, fine-tuned from didula-wso2/Qwen3-8B_julia_alpaca_ep4sft_16bit_vllm. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. With a 32768 token context length, it is optimized for specific planning tasks, building upon its base Julia Alpaca fine-tuning.
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Model Overview
The didula-wso2/Qwen3-8B_julia_planning-ep2sft_16bit_vllm is an 8 billion parameter Qwen3-based language model developed by didula-wso2. It is a fine-tuned iteration, building upon the didula-wso2/Qwen3-8B_julia_alpaca_ep4sft_16bit_vllm base model.
Key Training Details
- Base Model: Fine-tuned from
didula-wso2/Qwen3-8B_julia_alpaca_ep4sft_16bit_vllm. - Training Efficiency: This model was trained with a focus on speed, utilizing Unsloth and Huggingface's TRL library, resulting in a 2x faster training process.
- Context Length: The model supports a substantial context length of 32768 tokens.
Potential Use Cases
Given its fine-tuning lineage and the specific naming convention ("planning-ep2sft"), this model is likely specialized for:
- Planning-related tasks: Its training suggests an optimization for generating or assisting with planning sequences.
- Julia-specific applications: Building on a "Julia Alpaca" base, it may perform well in contexts involving the Julia programming language or related computational tasks.
This model is released under the Apache-2.0 license.