Text Generation
Transformers
Safetensors
qwen2
coder
code
agent
conversational
text-generation-inference
Instructions to use AdminReal/NexusCoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AdminReal/NexusCoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AdminReal/NexusCoder") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AdminReal/NexusCoder") model = AutoModelForCausalLM.from_pretrained("AdminReal/NexusCoder", 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 AdminReal/NexusCoder with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AdminReal/NexusCoder" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AdminReal/NexusCoder", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/AdminReal/NexusCoder
- SGLang
How to use AdminReal/NexusCoder 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 "AdminReal/NexusCoder" \ --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": "AdminReal/NexusCoder", "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 "AdminReal/NexusCoder" \ --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": "AdminReal/NexusCoder", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use AdminReal/NexusCoder with Docker Model Runner:
docker model run hf.co/AdminReal/NexusCoder
| """Code Formatter - Format code samples cho training.""" | |
| from __future__ import annotations | |
| import re | |
| from typing import Dict, Any, List, Optional | |
| class CodeFormatter: | |
| """Format code samples cho training. | |
| Features: | |
| - Strip excessive blank lines | |
| - Normalize indentation | |
| - Add language tags to code blocks | |
| - Wrap code in markdown fences if needed | |
| - Detect language automatically | |
| """ | |
| LANG_BY_EXT = { | |
| ".py": "python", ".js": "javascript", ".ts": "typescript", | |
| ".go": "go", ".rs": "rust", ".java": "java", | |
| ".c": "c", ".cpp": "cpp", ".h": "c", ".hpp": "cpp", | |
| ".cs": "csharp", ".rb": "ruby", ".php": "php", | |
| ".swift": "swift", ".kt": "kotlin", ".scala": "scala", | |
| ".sql": "sql", ".sh": "bash", ".bash": "bash", | |
| ".html": "html", ".css": "css", ".json": "json", | |
| ".yaml": "yaml", ".yml": "yaml", ".toml": "toml", | |
| ".xml": "xml", ".md": "markdown", | |
| } | |
| # Language detection patterns | |
| LANG_PATTERNS = { | |
| "python": [r"^\s*def\s+\w+", r"^\s*class\s+\w+", r"^\s*import\s+\w+", r"^\s*from\s+\w+\s+import"], | |
| "javascript": [r"^\s*function\s+\w+", r"^\s*const\s+\w+\s*=", r"^\s*let\s+\w+\s*=", r"=>\s*\{?"], | |
| "typescript": [r":\s*(string|number|boolean|void|any)\b", r"interface\s+\w+", r"type\s+\w+\s*="], | |
| "go": [r"^\s*func\s+\w+", r"^\s*package\s+\w+", r"^\s*import\s+\("], | |
| "rust": [r"^\s*fn\s+\w+", r"^\s*impl\s+\w+", r"^\s*use\s+\w+", r"^\s*let\s+mut\s+"], | |
| "java": [r"^\s*public\s+class\s+\w+", r"^\s*private\s+\w+\s+\w+", r"^\s*import\s+java\."], | |
| "c": [r"^\s*#include\s*<", r"^\s*int\s+main\s*\("], | |
| "cpp": [r"^\s*#include\s*<", r"^\s*std::", r"^\s*template\s*<"], | |
| } | |
| def detect_language(self, code: str, filename: Optional[str] = None) -> Optional[str]: | |
| """Detect programming language of code.""" | |
| if filename: | |
| import os | |
| ext = os.path.splitext(filename)[1].lower() | |
| if ext in self.LANG_BY_EXT: | |
| return self.LANG_BY_EXT[ext] | |
| # Pattern matching | |
| for lang, patterns in self.LANG_PATTERNS.items(): | |
| for pattern in patterns: | |
| if re.search(pattern, code, re.MULTILINE): | |
| return lang | |
| return None | |
| def format(self, code: str, language: Optional[str] = None) -> str: | |
| """Format code sample.""" | |
| # Detect language if not provided | |
| if not language: | |
| language = self.detect_language(code) or "" | |
| # Strip trailing whitespace on each line | |
| lines = [line.rstrip() for line in code.splitlines()] | |
| # Remove excessive blank lines (max 2 consecutive) | |
| formatted_lines = [] | |
| blank_count = 0 | |
| for line in lines: | |
| if line.strip() == "": | |
| blank_count += 1 | |
| if blank_count <= 2: | |
| formatted_lines.append("") | |
| else: | |
| blank_count = 0 | |
| formatted_lines.append(line) | |
| # Remove leading/trailing blank lines | |
| while formatted_lines and formatted_lines[0] == "": | |
| formatted_lines.pop(0) | |
| while formatted_lines and formatted_lines[-1] == "": | |
| formatted_lines.pop() | |
| code_clean = "\n".join(formatted_lines) | |
| return code_clean | |
| def wrap_in_markdown(self, code: str, language: Optional[str] = None) -> str: | |
| """Wrap code in markdown fence.""" | |
| if not language: | |
| language = self.detect_language(code) or "" | |
| return f"```{language}\n{code}\n```" | |
| def process(self, sample: Dict[str, Any]) -> Dict[str, Any]: | |
| """Process a code sample.""" | |
| sample = dict(sample) | |
| text = sample.get("text", "") | |
| language = sample.get("language") or sample.get("metadata", {}).get("language") | |
| # Check if it's code | |
| is_code = ( | |
| sample.get("language") or | |
| sample.get("metadata", {}).get("language") or | |
| self.detect_language(text) is not None | |
| ) | |
| if is_code: | |
| formatted = self.format(text, language) | |
| sample["text"] = formatted | |
| sample["metadata"] = sample.get("metadata", {}) | |
| sample["metadata"]["formatted"] = True | |
| if not language: | |
| language = self.detect_language(text) | |
| sample["metadata"]["detected_language"] = language | |
| return sample | |