Text Generation
Transformers
Safetensors
RWKV
English
fwkv
rosa
linear-transformer
recurrent-neural-network
chat
ultrachat
custom-architecture
custom_code
Instructions to use FWKV/FWKV-ROSA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FWKV/FWKV-ROSA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="FWKV/FWKV-ROSA", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("FWKV/FWKV-ROSA", trust_remote_code=True, device_map="auto") - RWKV
How to use FWKV/FWKV-ROSA with RWKV:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use FWKV/FWKV-ROSA with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "FWKV/FWKV-ROSA" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FWKV/FWKV-ROSA", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/FWKV/FWKV-ROSA
- SGLang
How to use FWKV/FWKV-ROSA 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 "FWKV/FWKV-ROSA" \ --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": "FWKV/FWKV-ROSA", "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 "FWKV/FWKV-ROSA" \ --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": "FWKV/FWKV-ROSA", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use FWKV/FWKV-ROSA with Docker Model Runner:
docker model run hf.co/FWKV/FWKV-ROSA
| { | |
| "accum_steps": 4, | |
| "auto_map": { | |
| "AutoModelForCausalLM": "modeling_fwkv.FWKVLanguageModel" | |
| }, | |
| "architectures": [ | |
| "FWKVLanguageModel" | |
| ], | |
| "batch_size": 8, | |
| "ce_chunk": 512, | |
| "d_emb": 128, | |
| "d_model": 512, | |
| "dataset_name": "HuggingFaceH4/ultrachat_200k", | |
| "dtype": "float32", | |
| "epochs": 2, | |
| "ffn_mult": 4, | |
| "grad_clip": 1.0, | |
| "lr": 0.0003, | |
| "max_train_examples": 20000, | |
| "max_val_examples": 1000, | |
| "model_type": "fwkv", | |
| "n_layers": 14, | |
| "seq_len": 1024, | |
| "tie_word_embeddings": true, | |
| "train_split": "train_sft", | |
| "transformers_version": "5.0.0", | |
| "val_split": "test_sft", | |
| "vocab_size": 50259, | |
| "wkv_floor": 0.1 | |
| } | |