DCAgent/a1-inferredbugs
DCAgent/a1-inferredbugs is an 8 billion parameter causal language model fine-tuned from Qwen/Qwen3-8B. This model is specifically trained on the /e/scratch/jureap59/raoof1/sft_data/hf_hub/datasets--DCAgent--inferredbugs-sandboxes_glm_4.7_traces_jupiter/snapshots/fa94a8d5ac1d6f3953e1d93f260ce88f5da381ff_thinking_preprocessed dataset, suggesting an optimization for tasks related to bug inference or analysis within sandboxed environments. It features a 32768 token context length, making it suitable for processing extensive code or trace data.
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Overview
DCAgent/a1-inferredbugs is an 8 billion parameter language model, fine-tuned from the Qwen/Qwen3-8B architecture. This model was developed by DCAgent and trained using a specific dataset: /e/scratch/jureap59/raoof1/sft_data/hf_hub/datasets--DCAgent--inferredbugs-sandboxes_glm_4.7_traces_jupiter/snapshots/fa94a8d5ac1d6f3953e1d93f260ce88f5da381ff_thinking_preprocessed. The fine-tuning process involved 7 epochs with a learning rate of 4e-05 and a total training batch size of 16 across 16 GPUs.
Key Characteristics
- Base Model: Qwen3-8B
- Parameter Count: 8 billion
- Context Length: 32768 tokens
- Training Data: Specialized dataset focused on "inferredbugs-sandboxes_glm_4.7_traces_jupiter", indicating a potential specialization in bug detection, analysis, or related tasks within sandboxed or traced environments.
Training Details
The model was trained using the following key hyperparameters:
- Optimizer: AdamW_Torch_Fused with betas=(0.9, 0.98) and epsilon=1e-08.
- LR Scheduler: Cosine type with a warmup ratio of 0.1.
- Epochs: 7.0
Intended Use Cases
While specific intended uses and limitations are not detailed in the provided README, the training data suggests that this model is likely optimized for tasks involving the analysis of traces or sandboxed environments to infer or identify bugs. Its large context window of 32768 tokens would be beneficial for processing extensive logs, code snippets, or execution traces.