Text Generation
Transformers
Safetensors
PyTorch
English
rose_x1
causal-lm
custom-architecture
rose-x1
rose-medium
custom_code
Instructions to use GODELEV/Rose-Medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GODELEV/Rose-Medium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="GODELEV/Rose-Medium", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("GODELEV/Rose-Medium", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use GODELEV/Rose-Medium with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "GODELEV/Rose-Medium" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "GODELEV/Rose-Medium", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/GODELEV/Rose-Medium
- SGLang
How to use GODELEV/Rose-Medium with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "GODELEV/Rose-Medium" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "GODELEV/Rose-Medium", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "GODELEV/Rose-Medium" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "GODELEV/Rose-Medium", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use GODELEV/Rose-Medium with Docker Model Runner:
docker model run hf.co/GODELEV/Rose-Medium
| """HuggingFace configuration for the Rose X1 architecture.""" | |
| from transformers import PretrainedConfig | |
| class RoseX1Config(PretrainedConfig): | |
| model_type = "rose_x1" | |
| def __init__( | |
| self, | |
| vocab_size=32768, | |
| hidden_size=512, | |
| intermediate_size=1720, | |
| num_hidden_layers=24, | |
| num_attention_heads=8, | |
| num_key_value_heads=2, | |
| head_dim=None, | |
| max_position_embeddings=2048, | |
| hidden_act="silu", | |
| rms_norm_eps=1e-5, | |
| attention_bias=False, | |
| mlp_bias=False, | |
| attention_dropout=0.0, | |
| tie_word_embeddings=True, | |
| rope_theta=100000.0, | |
| rope_scaling=None, | |
| initializer_range=0.02, | |
| use_cache=True, | |
| # ββ Rose X1 specifics ββββββββββββββββββββββββββββββββββββββββββββββ | |
| use_qk_norm=True, | |
| refresh_gate_enabled=True, | |
| refresh_gate_inject_layers=None, | |
| refresh_gate_kernel_size=9, | |
| **kwargs, | |
| ): | |
| self.vocab_size = vocab_size | |
| self.hidden_size = hidden_size | |
| self.intermediate_size = intermediate_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| self.num_key_value_heads = num_key_value_heads | |
| self.head_dim = head_dim if head_dim is not None else hidden_size // num_attention_heads | |
| self.max_position_embeddings = max_position_embeddings | |
| self.hidden_act = hidden_act | |
| self.rms_norm_eps = rms_norm_eps | |
| self.attention_bias = attention_bias | |
| self.mlp_bias = mlp_bias | |
| self.attention_dropout = attention_dropout | |
| self.tie_word_embeddings = tie_word_embeddings | |
| self.rope_theta = rope_theta | |
| self.rope_scaling = rope_scaling | |
| self.initializer_range = initializer_range | |
| self.use_cache = use_cache | |
| self.use_qk_norm = use_qk_norm | |
| self.refresh_gate_enabled = refresh_gate_enabled | |
| self.refresh_gate_inject_layers = ( | |
| list(refresh_gate_inject_layers) if refresh_gate_inject_layers else [] | |
| ) | |
| self.refresh_gate_kernel_size = refresh_gate_kernel_size | |
| super().__init__(**kwargs) |