Zero-Shot Classification
Transformers
Safetensors
qwen3_5
feature-extraction
security
code-review
ai-agents
decision-model
d3
system-one
pre-release
custom_code
Instructions to use stonefish-labs/d3-mini-agentcode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stonefish-labs/d3-mini-agentcode with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="stonefish-labs/d3-mini-agentcode", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("stonefish-labs/d3-mini-agentcode", trust_remote_code=True) model = AutoModel.from_pretrained("stonefish-labs/d3-mini-agentcode", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Card fact-check: usage snippet, long inputs, 4090 follow-up, limits, privacy, sources
124bf0b verified |
Download SOURCES.md from stonefish-labs/d3-mini-agentcode: direct link, hf CLI and curl.
- Browser
- Download file 3.24 kB
-
https://huggingface.co/stonefish-labs/d3-mini-agentcode/resolve/main/SOURCES.md
- Command line
-
hf download hf://stonefish-labs/d3-mini-agentcode/SOURCES.md
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curl -L -o SOURCES.md https://huggingface.co/stonefish-labs/d3-mini-agentcode/resolve/main/SOURCES.md
3.24 kB
| # Training data sources and attributions (d3-mini-agentcode, pre-release) | |
| The model weights are released under Apache-2.0. Training data is not redistributed. The sources below are credited as | |
| their cards ask. Our processing: extraction of agent diffs and executed snippets, filtering, and inserting authored inert | |
| examples into some items. | |
| ## Agent diffs (benign examples) | |
| | dataset | revision | licence | items | | |
| |---|---|---|---| | |
| | [nvidia/SWE-Zero-openhands-trajectories](https://huggingface.co/datasets/nvidia/SWE-Zero-openhands-trajectories) | `7b3cd106d00f60918e722d33a1d74bc67072a7ea` | CC-BY-4.0 (NVIDIA) | 1,446 | | |
| | [nvidia/SWE-Hero-openhands-trajectories](https://huggingface.co/datasets/nvidia/SWE-Hero-openhands-trajectories) | `150bc119e52c647216fce285fd801f16b6fd745b` | CC-BY-4.0 (NVIDIA) | 279 | | |
| | [nebius/SWE-rebench-openhands-trajectories](https://huggingface.co/datasets/nebius/SWE-rebench-openhands-trajectories) | `35455389ab51bf5e2306bfd436ef72d0f98bf882` | CC-BY-4.0 (Nebius) | 389 | | |
| | [sweagent/iter2-rl-rollouts](https://huggingface.co/datasets/sweagent/iter2-rl-rollouts) | `ec07602618bc22d56e71112fc8e81943fcbd28be` | MIT | 370 | | |
| | [nebius/SWE-agent-trajectories](https://huggingface.co/datasets/nebius/SWE-agent-trajectories) | `68195a1450865274106246d0d0296a1d6807b88e` | CC-BY-4.0 (Nebius) | 16 | | |
| The code in these diffs keeps its own repository's licence. We recorded a permissive licence for 1,725 of the 2,500 | |
| items. For the other 775 we didn't look it up. | |
| ## Code-mode snippets (benign examples) | |
| | dataset | revision | licence | | |
| |---|---|---| | |
| | [lambda/hermes-agent-reasoning-traces](https://huggingface.co/datasets/lambda/hermes-agent-reasoning-traces) | `b92885e4f0161d4b2536512710e004d4892cac6e` | Apache-2.0 | | |
| | [snorkelai/Tau2-Bench-Verified-Airline-With-Code-Agents](https://huggingface.co/datasets/snorkelai/Tau2-Bench-Verified-Airline-With-Code-Agents) | `23e3afcdd9492e9e6cc240c124be07665a8d211b` | Apache-2.0 (Snorkel AI) | | |
| | [snorkelai/Tau2-Bench-Airline-With-Code-Agents](https://huggingface.co/datasets/snorkelai/Tau2-Bench-Airline-With-Code-Agents) | `81702cbf1e8161c8cfac732578898a73e23813fe` | Apache-2.0 (Snorkel AI) | | |
| | [xingyaoww/code-act](https://huggingface.co/datasets/xingyaoww/code-act) | `afba34367a8609a1d0044eded531548ab71a58cf` | Apache-2.0. CodeAct: Wang et al., *Executable Code Actions Elicit Better LLM Agents*, ICML 2024 | | |
| ## Other training data | |
| - **A small set of scrubbed snippets from our own agent sessions** (577), used as benign examples only. Not released. | |
| - **Authored inert examples:** out-of-scope and malicious changes, in-scope controls and task-swap pairs, written by | |
| DeepSeek Flash into the public items above. Network destinations and secrets are stand-ins. | |
| - **Package-level examples:** public npm/PyPI packages, and malicious samples from the | |
| [DataDog malicious-software-packages-dataset](https://github.com/DataDog/malicious-software-packages-dataset). | |
| Package scanning is out of scope for this release. | |
| ## Base model | |
| [vllm-sr/d3-mini](https://huggingface.co/vllm-sr/d3-mini) @ `df99bf4ca4418bc6908ee68e9b7479af1f3b8800` (Apache-2.0, | |
| vLLM Semantic Router Team), built on [Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B) (Apache-2.0). | |