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 Quality Processor for Nexus Coder v0.3 | |
| ============================================ | |
| Scores Python code samples (1-10) based on quality signals: | |
| - Has docstring | |
| - Has type hints | |
| - No `print` statements (in non-test code) | |
| - No `eval` / `exec` / `__import__` | |
| - No bare `except:` clauses | |
| - Reasonable length (10-500 lines) | |
| - Has adjacent test file (bonus, requires file path) | |
| Samples below `min_score` (default 6.0) are filtered out. | |
| Author: Hieu Louis (2026) | |
| """ | |
| from __future__ import annotations | |
| import ast | |
| import re | |
| from typing import Dict | |
| _BAD_PATTERNS = [ | |
| (r"\beval\s*\(", "uses eval"), | |
| (r"\bexec\s*\(", "uses exec"), | |
| (r"\b__import__\s*\(", "uses __import__"), | |
| (r"\bassert\s+\w+\s*==\s*", "uses assert for tests (fine in tests, bad elsewhere)"), | |
| ] | |
| _BARE_EXCEPT = re.compile(r"\bexcept\s*:") | |
| _PRINT = re.compile(r"^\s*print\s*\(", re.MULTILINE) | |
| def score_python_code(code: str, is_test_file: bool = False) -> Dict[str, float]: | |
| """Score a Python code sample 0-10. Returns dict of factor → score contribution.""" | |
| factors: Dict[str, float] = {} | |
| # Try parsing as AST | |
| try: | |
| tree = ast.parse(code) | |
| except SyntaxError: | |
| return {"_invalid": 0.0, "_total": 0.0} | |
| except Exception: | |
| return {"_invalid": 0.0, "_total": 0.0} | |
| # Has docstring (module-level or first function)? | |
| has_docstring = ( | |
| (ast.get_docstring(tree) is not None) or | |
| any(isinstance(n, (ast.FunctionDef, ast.AsyncFunctionDef, ast.ClassDef)) and ast.get_docstring(n) for n in ast.walk(tree)) | |
| ) | |
| if has_docstring: | |
| factors["has_docstring"] = 1.5 | |
| # Type hints? | |
| typed_funcs = 0 | |
| total_funcs = 0 | |
| for node in ast.walk(tree): | |
| if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)): | |
| total_funcs += 1 | |
| if node.returns is not None or any(a.annotation for a in node.args.args): | |
| typed_funcs += 1 | |
| if total_funcs > 0 and typed_funcs / total_funcs > 0.3: | |
| factors["has_type_hints"] = 1.0 | |
| # No bare except | |
| has_bare_except = bool(_BARE_EXCEPT.search(code)) | |
| if not has_bare_except: | |
| factors["no_bare_except"] = 1.0 | |
| # No eval/exec/__import__ | |
| has_bad = False | |
| for pattern, _msg in _BAD_PATTERNS: | |
| if re.search(pattern, code): | |
| has_bad = True | |
| break | |
| if not has_bad: | |
| factors["no_eval"] = 1.0 | |
| # Print usage (allowed in tests) | |
| if not is_test_file: | |
| if not _PRINT.search(code): | |
| factors["no_print"] = 0.5 | |
| # Reasonable length | |
| n_lines = code.count("\n") + 1 | |
| if 10 <= n_lines <= 500: | |
| factors["reasonable_length"] = 1.0 | |
| elif 5 <= n_lines <= 1000: | |
| factors["reasonable_length"] = 0.5 | |
| # Bonus for tests | |
| if is_test_file: | |
| factors["has_test"] = 2.0 | |
| total = sum(factors.values()) | |
| factors["_total"] = min(10.0, total) | |
| return factors | |
| def score_code(code: str, language: str = "python", is_test_file: bool = False) -> Dict[str, float]: | |
| """Dispatch to language-specific scorer.""" | |
| if language == "python": | |
| return score_python_code(code, is_test_file=is_test_file) | |
| # For other languages, return neutral score | |
| return {"_total": 6.0, "_unimplemented_lang": 1.0} | |
| class CodeQualityProcessor: | |
| """Filter / tag samples by code quality score.""" | |
| def __init__( | |
| self, | |
| min_score: float = 6.0, | |
| is_test_file_fn=None, | |
| ): | |
| self.min_score = min_score | |
| self.is_test_file_fn = is_test_file_fn or (lambda path: path and "test" in path.lower()) | |
| def score(self, code: str, language: str = "python", path: str = "") -> float: | |
| is_test = bool(self.is_test_file_fn(path)) | |
| result = score_code(code, language=language, is_test_file=is_test) | |
| return result.get("_total", 0.0) | |
| def keep(self, code: str, language: str = "python", path: str = "") -> bool: | |
| return self.score(code, language=language, path=path) >= self.min_score | |
| def tag(self, sample: Dict) -> Dict: | |
| code = sample.get("content", sample.get("code", sample.get("text", ""))) | |
| lang = sample.get("lang", sample.get("language", "python")) | |
| path = sample.get("path", "") | |
| sample["code_quality_score"] = self.score(code, language=lang, path=path) | |
| return sample | |
| def batch_filter(self, samples): | |
| for s in samples: | |
| code = s.get("content", s.get("code", s.get("text", ""))) | |
| lang = s.get("lang", s.get("language", "python")) | |
| path = s.get("path", "") | |
| if self.keep(code, language=lang, path=path): | |
| yield s | |
| __all__ = ["score_python_code", "score_code", "CodeQualityProcessor"] | |