Instructions to use rwkv-x-dev/v5-EagleX-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rwkv-x-dev/v5-EagleX-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="rwkv-x-dev/v5-EagleX-7B", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("rwkv-x-dev/v5-EagleX-7B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use rwkv-x-dev/v5-EagleX-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rwkv-x-dev/v5-EagleX-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rwkv-x-dev/v5-EagleX-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/rwkv-x-dev/v5-EagleX-7B
- SGLang
How to use rwkv-x-dev/v5-EagleX-7B 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 "rwkv-x-dev/v5-EagleX-7B" \ --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": "rwkv-x-dev/v5-EagleX-7B", "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 "rwkv-x-dev/v5-EagleX-7B" \ --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": "rwkv-x-dev/v5-EagleX-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use rwkv-x-dev/v5-EagleX-7B with Docker Model Runner:
docker model run hf.co/rwkv-x-dev/v5-EagleX-7B
File size: 605 Bytes
69e9aad | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | {
"architectures": [
"RwkvForCausalLM"
],
"auto_map": {
"AutoConfig": "configuration_rwkv5.Rwkv5Config",
"AutoModelForCausalLM": "modeling_rwkv5.Rwkv5ForCausalLM"
},
"attention_hidden_size": 4096,
"bos_token_id": 0,
"context_length": 4096,
"eos_token_id": 0,
"head_size": 64,
"hidden_size": 4096,
"intermediate_size": null,
"layer_norm_epsilon": 1e-05,
"model_type": "rwkv5",
"model_version": "5_2",
"num_hidden_layers": 32,
"rescale_every": 6,
"tie_word_embeddings": false,
"transformers_version": "4.34.0",
"use_cache": true,
"vocab_size": 65536
} |