Instructions to use Sh2425/Dolphy-1.2-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sh2425/Dolphy-1.2-Base with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Sh2425/Dolphy-1.2-Base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use Sh2425/Dolphy-1.2-Base with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Sh2425/Dolphy-1.2-Base to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Sh2425/Dolphy-1.2-Base to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Sh2425/Dolphy-1.2-Base to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Sh2425/Dolphy-1.2-Base", max_seq_length=2048, )
| from transformers import PreTrainedModel | |
| from transformers.models.qwen3.modeling_qwen3 import Qwen3Model, Qwen3Config | |
| import torch.nn as nn | |
| class Dolphy1ForCausalLM(PreTrainedModel): | |
| config_class = Qwen3Config | |
| def __init__(self, config): | |
| super().__init__(config) | |
| self.model = Qwen3Model(config) | |
| # If your router was saved as part of the model, this will load it automatically. | |
| # No need to redefine or reattach anything here. | |
| def forward(self, input_ids, attention_mask=None, **kwargs): | |
| return self.model(input_ids=input_ids, attention_mask=attention_mask, **kwargs) |