Instructions to use haoranxu/ALMA-13B-R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use haoranxu/ALMA-13B-R with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="haoranxu/ALMA-13B-R")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("haoranxu/ALMA-13B-R") model = AutoModelForCausalLM.from_pretrained("haoranxu/ALMA-13B-R") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use haoranxu/ALMA-13B-R with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "haoranxu/ALMA-13B-R" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "haoranxu/ALMA-13B-R", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/haoranxu/ALMA-13B-R
- SGLang
How to use haoranxu/ALMA-13B-R 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 "haoranxu/ALMA-13B-R" \ --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": "haoranxu/ALMA-13B-R", "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 "haoranxu/ALMA-13B-R" \ --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": "haoranxu/ALMA-13B-R", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use haoranxu/ALMA-13B-R with Docker Model Runner:
docker model run hf.co/haoranxu/ALMA-13B-R
When there is a more flexible prompt, the model tends not to output
The model seems to be particularly sensitive to prompts. When I use "Translate this from English to Chinese:\nEnglish: " + user_prompt + "\nChinese:" or "Translate this from Chinese to English:\nChinese: " + user_prompt + "\nEnglish:", it can output the right answer.
from openai import OpenAI
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8080/v1"
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
user_prompt = '''
When there is a more flexible prompt, the model tends not to output.
'''
prompt = "Translate this from English to Chinese:\nEnglish: " + user_prompt + "\nChinese:"
chat_response = client.completions.create(
model='ALMA',
prompt=prompt,
temperature=0.7,
top_p=0.95,
stream=True,
max_tokens=2048
)
for data in chat_response:
data = data.choices[0].text
print(data, end='', flush=True)
print()
But if I change the prompt more flexible, like "Translate this from English to Chinese or Chinese to English:\nOriginal text: " + user_prompt + "\nResult:", it will putput nothing.
Is this a limitation? Can the translation task be completed based on a more flexible prompt?
Thanks for the interest in our work! Yes, the model should be sensitive to the given translation prompt because the prompt the ALMA models used for training is:
Translate this from <source langauge> to <target language>:
<source language>: <source sentence>
<target language>:
For optimal results during inference, it is recommended that users utilize the fixed prompt as provided above.