Text Generation
Transformers
Safetensors
English
babylm
babylm-2026
mixture-of-experts
msit
xpertgpt
custom_code
Instructions to use anonym5035/converted_gpt_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anonym5035/converted_gpt_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="anonym5035/converted_gpt_2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("anonym5035/converted_gpt_2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use anonym5035/converted_gpt_2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "anonym5035/converted_gpt_2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "anonym5035/converted_gpt_2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/anonym5035/converted_gpt_2
- SGLang
How to use anonym5035/converted_gpt_2 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/converted_gpt_2" \ --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/converted_gpt_2", "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/converted_gpt_2" \ --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/converted_gpt_2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use anonym5035/converted_gpt_2 with Docker Model Runner:
docker model run hf.co/anonym5035/converted_gpt_2
| import os | |
| import re | |
| def sanitize_content(content): | |
| # 1. Replace paper title variations | |
| title_regex = re.compile( | |
| r"XpertGPT:\s*Mixture\s*of\s*Experts\s*with\s*Parallelized\s*Multi-Scale\s*Information\s*Transmission(\s*for\s*Data-Constrained\s*Pretraining)?", | |
| re.IGNORECASE | |
| ) | |
| content = title_regex.sub("AnonymousModel: Mixture of Experts with Parallelized Multi-Scale Information Transmission", content) | |
| # 2. Replace specific author names | |
| authors = [ | |
| (re.compile(r"Soham\s*Jain", re.IGNORECASE), "Anonymous Author"), | |
| (re.compile(r"Harsh\s*Singh", re.IGNORECASE), "Anonymous Author"), | |
| (re.compile(r"Divija\s*Dewan", re.IGNORECASE), "Anonymous Author"), | |
| (re.compile(r"Atul\s*Dev", re.IGNORECASE), "Anonymous Author"), | |
| (re.compile(r"\bSoham\b", re.IGNORECASE), "Anonymous"), | |
| (re.compile(r"\bHarsh\b", re.IGNORECASE), "Anonymous"), | |
| (re.compile(r"\bDivija\b", re.IGNORECASE), "Anonymous"), | |
| (re.compile(r"\bAtul\b", re.IGNORECASE), "Anonymous"), | |
| (re.compile(r"\bJain\b", re.IGNORECASE), "Anonymous"), | |
| (re.compile(r"\bDewan\b", re.IGNORECASE), "Anonymous"), | |
| ] | |
| # We should be careful about replacing "Singh" and "Dev" as they might appear in code/comments | |
| # but we can replace them if they are in context of citation/names. | |
| # To be safe, let's replace "Singh" and "Dev" only when capitalized as part of names. | |
| authors.extend([ | |
| (re.compile(r"\bSingh\b"), "Anonymous"), | |
| (re.compile(r"\bDev\b"), "Anonymous"), | |
| ]) | |
| for pattern, replacement in authors: | |
| content = pattern.sub(replacement, content) | |
| # 3. Anonymize Hugging Face username, repo URLs, and BibTeX citation keys | |
| content = re.sub(r"SRJ5035", "anonymous_user", content) | |
| content = re.sub(r"swi_glu_sw_64_16_8_4_xpert_gpt", "anonymous_model", content) | |
| content = re.sub(r"jain2026xpertgpt", "anonymous2026model", content, flags=re.IGNORECASE) | |
| return content | |
| def main(): | |
| target_dir = os.path.dirname(os.path.abspath(__file__)) | |
| print(f"[Sanitizer] Scanning folder: {target_dir}") | |
| files_to_sanitize = [] | |
| for root, dirs, files in os.walk(target_dir): | |
| # Skip git and cache directories | |
| if '.git' in dirs: | |
| dirs.remove('.git') | |
| if '__pycache__' in dirs: | |
| dirs.remove('__pycache__') | |
| for f in files: | |
| if f in ['sanitize_codebase.py', 'upload_project_hf.py']: | |
| continue | |
| # Sanitize text-based files | |
| if f.endswith(('.py', '.md', '.txt', '.cff', '.json', '.sh')): | |
| files_to_sanitize.append(os.path.join(root, f)) | |
| for filepath in files_to_sanitize: | |
| print(f"[Sanitizer] Sanitizing: {os.path.relpath(filepath, target_dir)}") | |
| try: | |
| with open(filepath, 'r', encoding='utf-8', errors='ignore') as f: | |
| content = f.read() | |
| sanitized = sanitize_content(content) | |
| with open(filepath, 'w', encoding='utf-8') as f: | |
| f.write(sanitized) | |
| except Exception as e: | |
| print(f"[Sanitizer] Error processing {filepath}: {e}") | |
| print("[Sanitizer] Sanitization complete!") | |
| if __name__ == "__main__": | |
| main() | |