Text Generation
Transformers
ONNX
Safetensors
echo_hybrid
text-generation-inference
conversational
custom_code
Instructions to use mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2
- SGLang
How to use mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2 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 "mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2 with Docker Model Runner:
docker model run hf.co/mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2
| """ | |
| echo_hybrid/__init__.py | |
| ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| Package init: registers echo_hybrid classes with HuggingFace AutoClass so | |
| that AutoConfig.from_pretrained() and AutoModelForCausalLM.from_pretrained() | |
| work transparently without trust_remote_code=True. | |
| Usage | |
| βββββ | |
| import echo_hybrid # must be imported before any AutoClass call | |
| model = AutoModelForCausalLM.from_pretrained( | |
| "models/Echo-Hybrid-0.5B-Base", | |
| device_map="auto", | |
| ) | |
| """ | |
| from transformers import AutoConfig, AutoModelForCausalLM | |
| from .configuration_hybrid import HybridEchoConfig | |
| from .dsrn_memory_block import DSRNMemoryInjector | |
| from .modeling_hybrid import HybridEchoCache, HybridEchoForCausalLM, HybridEchoModel | |
| # Register with HuggingFace so AutoClass routing works | |
| AutoConfig.register("echo_hybrid", HybridEchoConfig) | |
| AutoModelForCausalLM.register(HybridEchoConfig, HybridEchoForCausalLM) | |
| __all__ = [ | |
| "HybridEchoConfig", | |
| "DSRNMemoryInjector", | |
| "HybridEchoModel", | |
| "HybridEchoForCausalLM", | |
| "HybridEchoCache", | |
| ] | |