Text Generation
PEFT
Safetensors
Transformers
English
llama
lora
dpo
smollm2
trl
conversational
text-generation-inference
Instructions to use Subject-Emu-5259/NeuralAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Subject-Emu-5259/NeuralAI with PEFT:
Base model is not found.
- Transformers
How to use Subject-Emu-5259/NeuralAI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Subject-Emu-5259/NeuralAI", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Subject-Emu-5259/NeuralAI") model = AutoModelForCausalLM.from_pretrained("Subject-Emu-5259/NeuralAI", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Subject-Emu-5259/NeuralAI with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Subject-Emu-5259/NeuralAI" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Subject-Emu-5259/NeuralAI", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Subject-Emu-5259/NeuralAI
- SGLang
How to use Subject-Emu-5259/NeuralAI 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 "Subject-Emu-5259/NeuralAI" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Subject-Emu-5259/NeuralAI", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "Subject-Emu-5259/NeuralAI" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Subject-Emu-5259/NeuralAI", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Subject-Emu-5259/NeuralAI with Docker Model Runner:
docker model run hf.co/Subject-Emu-5259/NeuralAI
| # tools/file_manager.py | |
| # | |
| # Advanced file operations | |
| # - Search by content or filename | |
| # - Read/write/create/delete | |
| # - Batch operations | |
| # - Directory listing | |
| import os | |
| import re | |
| import shutil | |
| import fnmatch | |
| from typing import Dict, Any, List, Optional | |
| from pathlib import Path | |
| class FileManager: | |
| """Manage files and directories with advanced operations.""" | |
| def __init__(self, base_dir: str = "/home/workspace"): | |
| self.base_dir = Path(base_dir) | |
| def list_dir(self, path: str = ".", show_hidden: bool = False) -> Dict[str, Any]: | |
| """ | |
| List contents of a directory. | |
| Returns: | |
| { | |
| "success": bool, | |
| "path": str, | |
| "files": [{"name": str, "type": str, "size": int, "modified": str}], | |
| "directories": [{"name": str, "modified": str}], | |
| "total_files": int, | |
| "total_dirs": int | |
| } | |
| """ | |
| full_path = self.base_dir / path | |
| if not full_path.exists(): | |
| return { | |
| "success": False, | |
| "error": f"Path does not exist: {path}", | |
| "path": str(full_path), | |
| "files": [], | |
| "directories": [], | |
| "total_files": 0, | |
| "total_dirs": 0 | |
| } | |
| if not full_path.is_dir(): | |
| return { | |
| "success": False, | |
| "error": f"Path is not a directory: {path}", | |
| "path": str(full_path), | |
| "files": [], | |
| "directories": [], | |
| "total_files": 0, | |
| "total_dirs": 0 | |
| } | |
| files = [] | |
| directories = [] | |
| try: | |
| for item in full_path.iterdir(): | |
| # Skip hidden files unless requested | |
| if not show_hidden and item.name.startswith('.'): | |
| continue | |
| modified = os.path.getmtime(item) | |
| modified_str = str(modified) | |
| if item.is_file(): | |
| size = os.path.getsize(item) | |
| files.append({ | |
| "name": item.name, | |
| "type": item.suffix.lower() or "unknown", | |
| "size": size, | |
| "modified": modified_str | |
| }) | |
| elif item.is_dir(): | |
| directories.append({ | |
| "name": item.name, | |
| "modified": modified_str | |
| }) | |
| # Sort by name | |
| files.sort(key=lambda x: x["name"]) | |
| directories.sort(key=lambda x: x["name"]) | |
| return { | |
| "success": True, | |
| "path": str(full_path), | |
| "files": files, | |
| "directories": directories, | |
| "total_files": len(files), | |
| "total_dirs": len(directories) | |
| } | |
| except Exception as e: | |
| return { | |
| "success": False, | |
| "error": str(e), | |
| "path": str(full_path), | |
| "files": [], | |
| "directories": [], | |
| "total_files": 0, | |
| "total_dirs": 0 | |
| } | |
| def read_file(self, path: str, max_size: int = 100000) -> Dict[str, Any]: | |
| """ | |
| Read contents of a file. | |
| Returns: | |
| { | |
| "success": bool, | |
| "path": str, | |
| "content": str, | |
| "size": int, | |
| "lines": int | |
| } | |
| """ | |
| full_path = self.base_dir / path | |
| if not full_path.exists(): | |
| return { | |
| "success": False, | |
| "error": f"File does not exist: {path}", | |
| "path": str(full_path), | |
| "content": "", | |
| "size": 0, | |
| "lines": 0 | |
| } | |
| if not full_path.is_file(): | |
| return { | |
| "success": False, | |
| "error": f"Path is not a file: {path}", | |
| "path": str(full_path), | |
| "content": "", | |
| "size": 0, | |
| "lines": 0 | |
| } | |
| try: | |
| size = os.path.getsize(full_path) | |
| # Check if file is too large | |
| if size > max_size: | |
| return { | |
| "success": False, | |
| "error": f"File too large: {size} bytes (max: {max_size})", | |
| "path": str(full_path), | |
| "content": "", | |
| "size": size, | |
| "lines": 0 | |
| } | |
| # Try to read as text | |
| try: | |
| with open(full_path, 'r', encoding='utf-8') as f: | |
| content = f.read() | |
| lines = content.count('\n') + 1 | |
| return { | |
| "success": True, | |
| "path": str(full_path), | |
| "content": content, | |
| "size": size, | |
| "lines": lines | |
| } | |
| except UnicodeDecodeError: | |
| # Binary file | |
| return { | |
| "success": False, | |
| "error": "File is binary, cannot read as text", | |
| "path": str(full_path), | |
| "content": "", | |
| "size": size, | |
| "lines": 0, | |
| "is_binary": True | |
| } | |
| except Exception as e: | |
| return { | |
| "success": False, | |
| "error": str(e), | |
| "path": str(full_path), | |
| "content": "", | |
| "size": 0, | |
| "lines": 0 | |
| } | |
| def write_file(self, path: str, content: str) -> Dict[str, Any]: | |
| """ | |
| Write content to a file (creates or overwrites). | |
| Returns: | |
| { | |
| "success": bool, | |
| "path": str, | |
| "size": int | |
| } | |
| """ | |
| full_path = self.base_dir / path | |
| try: | |
| # Create parent directories if needed | |
| full_path.parent.mkdir(parents=True, exist_ok=True) | |
| with open(full_path, 'w', encoding='utf-8') as f: | |
| f.write(content) | |
| size = os.path.getsize(full_path) | |
| return { | |
| "success": True, | |
| "path": str(full_path), | |
| "size": size | |
| } | |
| except Exception as e: | |
| return { | |
| "success": False, | |
| "error": str(e), | |
| "path": str(full_path), | |
| "size": 0 | |
| } | |
| def delete(self, path: str) -> Dict[str, Any]: | |
| """ | |
| Delete a file or directory. | |
| Returns: | |
| { | |
| "success": bool, | |
| "path": str, | |
| "type": str ("file" or "directory") | |
| } | |
| """ | |
| full_path = self.base_dir / path | |
| if not full_path.exists(): | |
| return { | |
| "success": False, | |
| "error": f"Path does not exist: {path}", | |
| "path": str(full_path), | |
| "type": None | |
| } | |
| try: | |
| if full_path.is_file(): | |
| os.unlink(full_path) | |
| return { | |
| "success": True, | |
| "path": str(full_path), | |
| "type": "file" | |
| } | |
| elif full_path.is_dir(): | |
| shutil.rmtree(full_path) | |
| return { | |
| "success": True, | |
| "path": str(full_path), | |
| "type": "directory" | |
| } | |
| else: | |
| return { | |
| "success": False, | |
| "error": "Unknown path type", | |
| "path": str(full_path), | |
| "type": None | |
| } | |
| except Exception as e: | |
| return { | |
| "success": False, | |
| "error": str(e), | |
| "path": str(full_path), | |
| "type": None | |
| } | |
| def search(self, pattern: str, path: str = ".", search_content: bool = False, | |
| max_results: int = 50) -> Dict[str, Any]: | |
| """ | |
| Search for files by name or content. | |
| Returns: | |
| { | |
| "success": bool, | |
| "query": str, | |
| "results": [{"path": str, "line": int, "match": str}], | |
| "total": int | |
| } | |
| """ | |
| full_path = self.base_dir / path | |
| results = [] | |
| if not full_path.exists(): | |
| return { | |
| "success": False, | |
| "error": f"Path does not exist: {path}", | |
| "query": pattern, | |
| "results": [], | |
| "total": 0 | |
| } | |
| try: | |
| if search_content: | |
| # Search within file contents | |
| for root, dirs, files in os.walk(full_path): | |
| # Skip hidden directories and common ignore patterns | |
| dirs[:] = [d for d in dirs if not d.startswith('.') and | |
| d not in ['node_modules', '__pycache__', 'venv', '.git']] | |
| for filename in files: | |
| if filename.startswith('.'): | |
| continue | |
| filepath = Path(root) / filename | |
| # Skip binary files | |
| if filepath.suffix.lower() in ['.pyc', '.so', '.dll', '.exe', '.bin']: | |
| continue | |
| try: | |
| with open(filepath, 'r', encoding='utf-8', errors='ignore') as f: | |
| for line_num, line in enumerate(f, 1): | |
| if pattern.lower() in line.lower(): | |
| rel_path = filepath.relative_to(self.base_dir) | |
| results.append({ | |
| "path": str(rel_path), | |
| "line": line_num, | |
| "match": line.strip()[:100] | |
| }) | |
| if len(results) >= max_results: | |
| break | |
| except: | |
| pass | |
| if len(results) >= max_results: | |
| break | |
| if len(results) >= max_results: | |
| break | |
| else: | |
| # Search by filename | |
| for root, dirs, files in os.walk(full_path): | |
| dirs[:] = [d for d in dirs if not d.startswith('.')] | |
| for filename in files: | |
| if fnmatch.fnmatch(filename.lower(), f"*{pattern.lower()}*"): | |
| filepath = Path(root) / filename | |
| rel_path = filepath.relative_to(self.base_dir) | |
| results.append({ | |
| "path": str(rel_path), | |
| "line": 0, | |
| "match": filename | |
| }) | |
| if len(results) >= max_results: | |
| break | |
| if len(results) >= max_results: | |
| break | |
| return { | |
| "success": True, | |
| "query": pattern, | |
| "results": results, | |
| "total": len(results) | |
| } | |
| except Exception as e: | |
| return { | |
| "success": False, | |
| "error": str(e), | |
| "query": pattern, | |
| "results": [], | |
| "total": 0 | |
| } | |
| def copy(self, src: str, dst: str) -> Dict[str, Any]: | |
| """Copy a file or directory.""" | |
| src_path = self.base_dir / src | |
| dst_path = self.base_dir / dst | |
| if not src_path.exists(): | |
| return { | |
| "success": False, | |
| "error": f"Source does not exist: {src}", | |
| "src": str(src_path), | |
| "dst": str(dst_path) | |
| } | |
| try: | |
| dst_path.parent.mkdir(parents=True, exist_ok=True) | |
| if src_path.is_file(): | |
| shutil.copy2(src_path, dst_path) | |
| else: | |
| shutil.copytree(src_path, dst_path) | |
| return { | |
| "success": True, | |
| "src": str(src_path), | |
| "dst": str(dst_path) | |
| } | |
| except Exception as e: | |
| return { | |
| "success": False, | |
| "error": str(e), | |
| "src": str(src_path), | |
| "dst": str(dst_path) | |
| } | |
| def move(self, src: str, dst: str) -> Dict[str, Any]: | |
| """Move/rename a file or directory.""" | |
| src_path = self.base_dir / src | |
| dst_path = self.base_dir / dst | |
| if not src_path.exists(): | |
| return { | |
| "success": False, | |
| "error": f"Source does not exist: {src}", | |
| "src": str(src_path), | |
| "dst": str(dst_path) | |
| } | |
| try: | |
| dst_path.parent.mkdir(parents=True, exist_ok=True) | |
| shutil.move(str(src_path), str(dst_path)) | |
| return { | |
| "success": True, | |
| "src": str(src_path), | |
| "dst": str(dst_path) | |
| } | |
| except Exception as e: | |
| return { | |
| "success": False, | |
| "error": str(e), | |
| "src": str(src_path), | |
| "dst": str(dst_path) | |
| } | |
| def create_dir(self, path: str) -> Dict[str, Any]: | |
| """Create a directory.""" | |
| full_path = self.base_dir / path | |
| try: | |
| full_path.mkdir(parents=True, exist_ok=True) | |
| return { | |
| "success": True, | |
| "path": str(full_path) | |
| } | |
| except Exception as e: | |
| return { | |
| "success": False, | |
| "error": str(e), | |
| "path": str(full_path) | |
| } | |
| if __name__ == "__main__": | |
| # Test the file manager | |
| fm = FileManager() | |
| print("Listing workspace:") | |
| result = fm.list_dir() | |
| print(f"Files: {result['total_files']}, Dirs: {result['total_dirs']}") | |
| print("\nSearching for Python files:") | |
| result = fm.search(".py", search_content=False) | |
| print(f"Found {result['total']} files") | |