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
Download adapter/adapter_config.json from stonefish-labs/d3-mini-agentcode: direct link, hf CLI and curl.
- Browser
- Download file 1.33 kB
-
https://huggingface.co/stonefish-labs/d3-mini-agentcode/resolve/main/adapter/adapter_config.json
- Command line
-
hf download hf://stonefish-labs/d3-mini-agentcode/adapter/adapter_config.json
-
curl -L -o adapter_config.json https://huggingface.co/stonefish-labs/d3-mini-agentcode/resolve/main/adapter/adapter_config.json
1.33 kB
| { | |
| "alora_invocation_tokens": null, | |
| "alpha_pattern": {}, | |
| "arrow_config": null, | |
| "auto_mapping": { | |
| "base_model_class": "D3ReadoutModel", | |
| "parent_library": "d3train.model" | |
| }, | |
| "base_model_name_or_path": "vllm-sr/d3-mini", | |
| "bias": "none", | |
| "corda_config": null, | |
| "ensure_weight_tying": false, | |
| "eva_config": null, | |
| "exclude_modules": null, | |
| "fan_in_fan_out": false, | |
| "inference_mode": true, | |
| "init_lora_weights": true, | |
| "kasa_config": null, | |
| "layer_replication": null, | |
| "layers_pattern": null, | |
| "layers_to_transform": null, | |
| "loftq_config": {}, | |
| "lora_alpha": 32, | |
| "lora_bias": false, | |
| "lora_dropout": 0.05, | |
| "lora_ga_config": null, | |
| "megatron_config": null, | |
| "megatron_core": "megatron.core", | |
| "modules_to_save": null, | |
| "monteclora_config": null, | |
| "peft_type": "LORA", | |
| "peft_version": "0.21.2", | |
| "qalora_group_size": 16, | |
| "r": 16, | |
| "rank_pattern": {}, | |
| "revision": "df99bf4ca4418bc6908ee68e9b7479af1f3b8800", | |
| "target_modules": "^language_model\\.layers\\.\\d+\\.(self_attn|linear_attn|mlp)\\.(q_proj|k_proj|v_proj|o_proj|in_proj_qkv|in_proj_z|out_proj|gate_proj|up_proj|down_proj)$", | |
| "target_parameters": null, | |
| "task_type": null, | |
| "trainable_token_indices": null, | |
| "use_bdlora": null, | |
| "use_dora": false, | |
| "use_qalora": false, | |
| "use_rslora": false, | |
| "velora_config": null | |
| } |