Text Generation
Transformers
Safetensors
PEFT
English
honesty-alignment
confidence-calibration
lora
llm-alignment
Instructions to use Shiyunee/Honest-Qwen2.5-14B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shiyunee/Honest-Qwen2.5-14B-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Shiyunee/Honest-Qwen2.5-14B-Instruct")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Shiyunee/Honest-Qwen2.5-14B-Instruct", device_map="auto") - PEFT
How to use Shiyunee/Honest-Qwen2.5-14B-Instruct with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Shiyunee/Honest-Qwen2.5-14B-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Shiyunee/Honest-Qwen2.5-14B-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Shiyunee/Honest-Qwen2.5-14B-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Shiyunee/Honest-Qwen2.5-14B-Instruct
- SGLang
How to use Shiyunee/Honest-Qwen2.5-14B-Instruct 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 "Shiyunee/Honest-Qwen2.5-14B-Instruct" \ --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": "Shiyunee/Honest-Qwen2.5-14B-Instruct", "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 "Shiyunee/Honest-Qwen2.5-14B-Instruct" \ --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": "Shiyunee/Honest-Qwen2.5-14B-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Shiyunee/Honest-Qwen2.5-14B-Instruct with Docker Model Runner:
docker model run hf.co/Shiyunee/Honest-Qwen2.5-14B-Instruct
Download config.json from Shiyunee/Honest-Qwen2.5-14B-Instruct: direct link, hf CLI and curl.
- Browser
- Download file 148 Bytes
-
https://huggingface.co/Shiyunee/Honest-Qwen2.5-14B-Instruct/resolve/main/config.json
- Command line
-
hf download hf://Shiyunee/Honest-Qwen2.5-14B-Instruct/config.json
-
curl -L -o config.json https://huggingface.co/Shiyunee/Honest-Qwen2.5-14B-Instruct/resolve/main/config.json
148 Bytes
Invalid JSON:Unexpected token '#', "# used for"... is not valid JSON
| # used for count the download. | |
| { | |
| "model_type": "meta", | |
| "architectures": ["ModelCollection"], | |
| "submodels": ["lr_1e-4", "lr_5e-5", "lr_1e-5"] | |
| } | |