Instructions to use adamluc/pythia7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adamluc/pythia7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="adamluc/pythia7b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("adamluc/pythia7b") model = AutoModelForCausalLM.from_pretrained("adamluc/pythia7b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use adamluc/pythia7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "adamluc/pythia7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "adamluc/pythia7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/adamluc/pythia7b
- SGLang
How to use adamluc/pythia7b 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 "adamluc/pythia7b" \ --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": "adamluc/pythia7b", "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 "adamluc/pythia7b" \ --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": "adamluc/pythia7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use adamluc/pythia7b with Docker Model Runner:
docker model run hf.co/adamluc/pythia7b
Upload handler.py
Browse files- handler.py +14 -0
handler.py
ADDED
|
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import torch
|
| 2 |
+
from transformers import AutoTokenizer, AutoModelForCausalLM
|
| 3 |
+
|
| 4 |
+
class PythiaChatHandler:
|
| 5 |
+
def __init__(self, model_name="togethercomputer/Pythia-Chat-Base-7B"):
|
| 6 |
+
self.device = "cuda" if torch.cuda.is_available() else "cpu"
|
| 7 |
+
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
|
| 8 |
+
self.model = AutoModelForCausalLM.from_pretrained(model_name).to(self.device)
|
| 9 |
+
|
| 10 |
+
def __call__(self, input_text):
|
| 11 |
+
input_ids = self.tokenizer.encode(input_text, return_tensors="pt").to(self.device)
|
| 12 |
+
output_ids = self.model.generate(input_ids).to("cpu")
|
| 13 |
+
response_text = self.tokenizer.decode(output_ids[0], skip_special_tokens=True)
|
| 14 |
+
return response_text
|