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repliedto Abhisek987's post 1 day ago
New dataset: 24,922 real Python dependency compatibility experiments🧪
Every experiment installed a pair of Python packages into a fresh environment and tested three stages: resolution → import → runtime smoke test.
The finding that motivated this: resolvers only read published metadata. In this data, ~3,000 environments resolved successfully and still failed at import or smoke-test time. That gap is invisible to pip and uv, and this dataset makes it measurable.
What's inside:
- 21,490 development + 3,432 sealed validation experiments
- 41 unique packages, Python 3.8–3.14, Linux x86_64
- The 6 validation packages (boto3, botocore, s3transfer, celery, kombu, billiard) never appear in training — validation measures generalization to entirely unseen packages
- Full per-stage logs: exact commands, exit codes, stdout/stderr
- Normalized error text + Python exception types for every failure
- Wheel metadata with Python/ABI/platform tag compatibility decisions
- PEP 751 lockfile hashes
- 142 engineered features per experiment, ready for training
Outcome labels: pass, resolution_failure, import_failure, smoke_test_failure.
I used it to train two models for DepLab (a structured model + a fine-tuned ModernBERT on release notes). On the sealed validation set (entirely unseen packages) the structured model reached 90.0% accuracy and 100% precision when predicting failures.
CC BY 4.0. Load it directly:
```python
from datasets import load_dataset
ds = load_dataset("Abhisek987/deplab-dependency-compatibility", "experiments")
```
Dataset: https://huggingface.co/datasets/Abhisek987/deplab-dependency-compatibility
Zenodo DOI: https://doi.org/10.5281/zenodo.21729353
Code: https://github.com/Abhisek12378/DepLab
If you're working on ML for developer tools or software engineering research, I'd love to hear what you build with it. repliedto Abhisek987's post 1 day ago
New dataset: 24,922 real Python dependency compatibility experiments🧪
Every experiment installed a pair of Python packages into a fresh environment and tested three stages: resolution → import → runtime smoke test.
The finding that motivated this: resolvers only read published metadata. In this data, ~3,000 environments resolved successfully and still failed at import or smoke-test time. That gap is invisible to pip and uv, and this dataset makes it measurable.
What's inside:
- 21,490 development + 3,432 sealed validation experiments
- 41 unique packages, Python 3.8–3.14, Linux x86_64
- The 6 validation packages (boto3, botocore, s3transfer, celery, kombu, billiard) never appear in training — validation measures generalization to entirely unseen packages
- Full per-stage logs: exact commands, exit codes, stdout/stderr
- Normalized error text + Python exception types for every failure
- Wheel metadata with Python/ABI/platform tag compatibility decisions
- PEP 751 lockfile hashes
- 142 engineered features per experiment, ready for training
Outcome labels: pass, resolution_failure, import_failure, smoke_test_failure.
I used it to train two models for DepLab (a structured model + a fine-tuned ModernBERT on release notes). On the sealed validation set (entirely unseen packages) the structured model reached 90.0% accuracy and 100% precision when predicting failures.
CC BY 4.0. Load it directly:
```python
from datasets import load_dataset
ds = load_dataset("Abhisek987/deplab-dependency-compatibility", "experiments")
```
Dataset: https://huggingface.co/datasets/Abhisek987/deplab-dependency-compatibility
Zenodo DOI: https://doi.org/10.5281/zenodo.21729353
Code: https://github.com/Abhisek12378/DepLab
If you're working on ML for developer tools or software engineering research, I'd love to hear what you build with it. repliedto sergiopaniego's post 1 day ago
LFM2.5-2.6B just dropped!
and the @liquidai blog comes with some nice details about the training procedure, so let's analyze it.
basically, a full agent training pipeline but compressed into 2.6B
base model → SFT → specialized teachers per domain (SFT + RLVR) → on-policy distillation back into one student → agentic RL
the two most interesting stages
→ MOPD: the student generates, each prompt routes to its domain teacher for token-level feedback. teachers branch from the same SFT checkpoint, so their signal stays close to the student's distribution
→ agentic RL: multi-turn GRPO inside real harnesses (OpenClaw, Hermes Agent), one sandbox per rollout, a proxy captures token-level trajectories while the harness stays a black box
this makes a 2.6B that beats much larger models on instruction following and tool use
SFT, distillation, RL, RL envs: exactly what we're covering in our Training Agents livestream series (next one coming soon!)
→ model: https://huggingface.co/LiquidAI/LFM2.5-2.6B
→ blog: https://www.liquid.ai/blog/lfm2-5-2-6b
→ live series: https://www.youtube.com/playlist?list=PLo2EIpI_JMQvQZm-kVlz4wY1vWF0LBcf5Organizations
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