Instructions to use IFM/AmberSafe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IFM/AmberSafe with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="IFM/AmberSafe")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("IFM/AmberSafe") model = AutoModelForCausalLM.from_pretrained("IFM/AmberSafe", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use IFM/AmberSafe with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "IFM/AmberSafe" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IFM/AmberSafe", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/IFM/AmberSafe
- SGLang
How to use IFM/AmberSafe 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 "IFM/AmberSafe" \ --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": "IFM/AmberSafe", "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 "IFM/AmberSafe" \ --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": "IFM/AmberSafe", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use IFM/AmberSafe with Docker Model Runner:
docker model run hf.co/IFM/AmberSafe
Commit ·
f23e579
1
Parent(s): 6355444
Update README.md
Browse files
README.md
CHANGED
|
@@ -56,7 +56,7 @@ tokenizer = LlamaTokenizer.from_pretrained("LLM360/AmberSafe")
|
|
| 56 |
model = LlamaForCausalLM.from_pretrained("LLM360/AmberSafe")
|
| 57 |
|
| 58 |
#template adapated from fastchat
|
| 59 |
-
template= "
|
| 60 |
|
| 61 |
prompt = "How do I mount a tv to drywall safely?"
|
| 62 |
|
|
|
|
| 56 |
model = LlamaForCausalLM.from_pretrained("LLM360/AmberSafe")
|
| 57 |
|
| 58 |
#template adapated from fastchat
|
| 59 |
+
template= "###Human: {prompt}\n###Assistant:"
|
| 60 |
|
| 61 |
prompt = "How do I mount a tv to drywall safely?"
|
| 62 |
|