Instructions to use LayerEight/Juniper-Qwen2.5-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use LayerEight/Juniper-Qwen2.5-14B with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-14B-Instruct") model = PeftModel.from_pretrained(base_model, "LayerEight/Juniper-Qwen2.5-14B") - Transformers
How to use LayerEight/Juniper-Qwen2.5-14B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LayerEight/Juniper-Qwen2.5-14B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("LayerEight/Juniper-Qwen2.5-14B", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use LayerEight/Juniper-Qwen2.5-14B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LayerEight/Juniper-Qwen2.5-14B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LayerEight/Juniper-Qwen2.5-14B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/LayerEight/Juniper-Qwen2.5-14B
- SGLang
How to use LayerEight/Juniper-Qwen2.5-14B 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 "LayerEight/Juniper-Qwen2.5-14B" \ --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": "LayerEight/Juniper-Qwen2.5-14B", "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 "LayerEight/Juniper-Qwen2.5-14B" \ --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": "LayerEight/Juniper-Qwen2.5-14B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use LayerEight/Juniper-Qwen2.5-14B with Docker Model Runner:
docker model run hf.co/LayerEight/Juniper-Qwen2.5-14B
File size: 134 Bytes
e215679 | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:134c577769ee66c1654998e8007b7f6114af0de62c0d223de5fe2e0eb1d6e19e
size 275819843
|