ricdomolm/talkie-1930-coder
The ricdomolm/talkie-1930-coder is a 13 billion parameter model developed by ricdomolm, fine-tuned for agentic software engineering tasks. Starting from the talkie-1930 base, it is specifically optimized for processing and generating code within the mini-swe-agent interaction format. This model demonstrates a pass@1 score of 4.48% ± 0.69 pp on the SWE-bench-Verified-Working-Harbor benchmark, making it suitable for automated code resolution and software development workflows. Its 32768-token context length supports complex coding challenges.
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ricdomolm/talkie-1930-coder: Agentic Software Engineering Model
This model, developed by ricdomolm, is a 13 billion parameter language model fine-tuned for agentic software engineering tasks. It is built upon the talkie-1930 base model and specifically optimized for the mini-swe-agent interaction format.
Key Capabilities & Performance
- Agentic Code Resolution: Fine-tuned on agentic software-engineering trajectories from the SWE-smith dataset.
- SWE-bench Performance: Achieves a pass@1 score of 4.48% ± 0.69 pp on the SWE-bench-Verified-Working-Harbor benchmark over 5 independent evaluation runs.
- Context Length: Supports a substantial 32768-token context window, enabling it to handle complex codebases and problem descriptions.
- Training Details: Trained using TRL
SFTTrainerwithadamw_torch_fusedoptimizer,bf16precision, and a maximum sequence length of 65,536, utilizingcompletion_only_loss.
Usage & Integration
- Requires
trust_remote_code=Truefor loading due to custom modeling code (modeling_talkie.py,configuration_talkie.py). - Designed for integration with
vLLMfor serving andmini-swe-agentfor driving agentic evaluations.
Companion Model
- A companion model,
ricdomolm/talkie-web-coder, exists, which uses the same training recipe but starts from a web-style data pre-trained base model, achieving 5.75% ± 1.04 pp on the same evaluation.