laion/exp_tas_top_k_64_traces
The laion/exp_tas_top_k_64_traces model is an 8 billion parameter language model fine-tuned from Qwen/Qwen3-8B. It was trained on the DCAgent/exp_tas_top_k_64_traces dataset with a context length of 32768 tokens. This model is specialized for tasks related to the specific dataset it was fine-tuned on, likely involving agent traces or similar sequential data.
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Model Overview
This model, laion/exp_tas_top_k_64_traces, is an 8 billion parameter language model derived from the Qwen/Qwen3-8B architecture. It has been fine-tuned specifically on the DCAgent/exp_tas_top_k_64_traces dataset, suggesting a specialization in processing or generating data related to agent traces or similar sequential decision-making processes.
Training Details
The fine-tuning process utilized a learning rate of 4e-05, a total training batch size of 16 (with a train_batch_size of 1 and gradient_accumulation_steps of 2), and ran for 7 epochs. The optimizer used was ADAMW_TORCH_FUSED with standard beta values and epsilon. A cosine learning rate scheduler with a 0.1 warmup ratio was employed. The training was distributed across 8 GPUs.
Key Characteristics
- Base Model: Qwen/Qwen3-8B
- Parameter Count: 8 billion
- Context Length: 32768 tokens
- Specialization: Fine-tuned on
DCAgent/exp_tas_top_k_64_tracesdataset, indicating potential expertise in agent-based or trace-related tasks.
Intended Use Cases
While specific use cases are not detailed in the provided information, its fine-tuning on a specialized dataset implies suitability for applications involving:
- Analysis of agent behaviors or traces.
- Generation of sequences or actions based on observed traces.
- Tasks requiring understanding or prediction within specific agent environments.