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srivarenya
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Model Details
Model Provider
srivarenya
Model Tree
Qwen/Qwen2.5-Coder-1.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
| Suite | Metric | base | this model |
|---|---|---|---|
| HumanEval | pass@1 | 68.9 | 70.7 |
| MBPP | pass@1 | 66.7 | 69.6 |
| Domain (held-out) | spec_to_code exec | 0.632 | 0.714 (+8.2) |
| Domain (held-out) | api_signature param-recall | 0.217 | 0.299 (+8.2) |
| Domain (held-out) | problem_solving exec | 0.700 | 0.713 (parity) |
The largest gains are on library/API capability (writing correct code from a spec, recalling API signatures) — the dimension HumanEval/MBPP are saturated on and can't measure. The repo's self-contained domain-eval notebook reproduces these.
python
from transformers import AutoModelForCausalLM, AutoTokenizertok = AutoTokenizer.from_pretrained("srivarenya/MoM-python-slm")model = AutoModelForCausalLM.from_pretrained( "srivarenya/MoM-python-slm", dtype="bfloat16", device_map="auto")Prompt with the training system prompt + a Python task; the model returns reasoning then code.
Next step in the pipeline: GRPO/RLVR against an execution-grounded reward to push past the instruct-tuning ceiling. Code, training recipe, and eval harnesses: project repository.