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
File size: 4,727 Bytes
eca5751 | 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 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 | """
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"]
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