Text Generation
Transformers
Safetensors
English
babylm
babylm-2026
mixture-of-experts
msit
xpertgpt
custom_code
Instructions to use anonym5035/temp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anonym5035/temp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="anonym5035/temp", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("anonym5035/temp", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use anonym5035/temp with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "anonym5035/temp" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "anonym5035/temp", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/anonym5035/temp
- SGLang
How to use anonym5035/temp 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 "anonym5035/temp" \ --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": "anonym5035/temp", "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 "anonym5035/temp" \ --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": "anonym5035/temp", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use anonym5035/temp with Docker Model Runner:
docker model run hf.co/anonym5035/temp
File size: 2,769 Bytes
3a7a00c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | import os
import sys
import re
import argparse
from huggingface_hub import HfApi
def upload_project(repo_name=None):
token = os.environ.get("HF_TOKEN")
if not token:
print("Error: HF_TOKEN environment variable not set!")
print("Please set it before running: set HF_TOKEN=your_token")
sys.exit(1)
if not repo_name:
repo_name = os.environ.get("HF_REPO_NAME", "temp")
local_dir = os.path.dirname(os.path.abspath(__file__))
api = HfApi(token=token)
try:
user_info = api.whoami()
username = user_info["name"]
print(f"[HF] Authenticated as: {username}")
except Exception as e:
print(f"[HF] Authentication failed: {e}")
sys.exit(1)
repo_id = f"{username}/{repo_name}"
print(f"[HF] Uploading modified sliding-window strict-small scripts to '{repo_id}' main branch...")
ignore_patterns = [
"**/__pycache__/*",
"**/*.pyc",
"**/hf_cache/*",
"**/nltk_data/*",
"**/checkpoints/*"
]
sensitive_ignores = []
token_pattern = re.compile(r"hf_[a-zA-Z0-9]{34}")
for root, dirs, files in os.walk(local_dir):
if "__pycache__" in root or "checkpoints" in root:
continue
for file in files:
file_path = os.path.join(root, file)
if file.endswith(('.bin', '.safetensors', '.zip', '.tar.gz', '.pkl')):
continue
try:
with open(file_path, "r", errors="ignore") as f:
content = f.read()
if token_pattern.search(content):
rel_path = os.path.relpath(file_path, local_dir)
rel_path_glob = rel_path.replace("\\", "/")
sensitive_ignores.append(rel_path_glob)
print(f" -> Warning: Sensitive file containing raw token excluded: {rel_path_glob}")
except Exception:
pass
all_ignores = ignore_patterns + sensitive_ignores
try:
api.upload_folder(
folder_path=local_dir,
repo_id=repo_id,
repo_type="model",
revision="main",
ignore_patterns=all_ignores,
commit_message="Update strict-small architecture files (sliding window [64, 16, 8, 4], ln3, ln_post_moe, no res3)"
)
print(f"\n[HF] Success! Scripts uploaded to: https://huggingface.co/{repo_id}/tree/main")
except Exception as e:
print(f"[HF] Upload failed: {e}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--repo-name", type=str, default=None, help="Hugging Face repository name")
args = parser.parse_args()
upload_project(args.repo_name)
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