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
| """ | |
| Script thu thập training data từ GitHub + HuggingFace | |
| ===================================================== | |
| Chạy script này để collect training data cho Nexus Coder v0.2. | |
| Sources: | |
| - GitHub repos (curated list trong nexus.data.collectors.github_collector.CURATED_REPOS) | |
| - HuggingFace datasets (curated list trong nexus.data.collectors.huggingface_collector.CURATED_DATASETS) | |
| - arXiv papers (curated queries) | |
| - Wikipedia (Vietnamese + English) | |
| - StackOverflow Q&A | |
| Usage: | |
| python scripts/collect_data.py --source github --max-repos 10 | |
| python scripts/collect_data.py --source huggingface --max-datasets 5 | |
| python scripts/collect_data.py --source all --output ./data/raw | |
| """ | |
| import sys | |
| import os | |
| import argparse | |
| import json | |
| import logging | |
| from pathlib import Path | |
| sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) | |
| logging.basicConfig( | |
| level=logging.INFO, | |
| format="%(asctime)s [%(levelname)s] %(message)s", | |
| ) | |
| logger = logging.getLogger(__name__) | |
| def collect_github(output_dir: str, max_repos: int = 10, token: str = None): | |
| """Collect code từ GitHub repos.""" | |
| from nexus.data.collectors.github_collector import GitHubCollector, CURATED_REPOS | |
| collector = GitHubCollector(token=token, cache_dir=os.path.join(output_dir, "github_cache")) | |
| repos = CURATED_REPOS[:max_repos] | |
| logger.info(f"Collecting from {len(repos)} GitHub repos...") | |
| output_file = os.path.join(output_dir, "github_code.jsonl") | |
| count = 0 | |
| with open(output_file, "w", encoding="utf-8") as f: | |
| for sample in collector.collect(repos): | |
| entry = { | |
| "text": sample.content, | |
| "source": f"github:{sample.repo}", | |
| "language": sample.language, | |
| "metadata": { | |
| "file_path": sample.file_path, | |
| "size": sample.size, | |
| "quality_score": sample.quality_score, | |
| }, | |
| } | |
| f.write(json.dumps(entry, ensure_ascii=False) + "\n") | |
| count += 1 | |
| if count % 100 == 0: | |
| logger.info(f" Collected {count} samples...") | |
| logger.info(f"✓ GitHub: {count} samples → {output_file}") | |
| return count | |
| def collect_huggingface(output_dir: str, max_datasets: int = 5, token: str = None): | |
| """Collect từ HuggingFace datasets.""" | |
| from nexus.data.collectors.huggingface_collector import HuggingFaceCollector, CURATED_DATASETS | |
| collector = HuggingFaceCollector(cache_dir=os.path.join(output_dir, "hf_cache"), token=token) | |
| datasets = CURATED_DATASETS[:max_datasets] | |
| logger.info(f"Collecting from {len(datasets)} HuggingFace datasets...") | |
| output_file = os.path.join(output_dir, "hf_data.jsonl") | |
| count = 0 | |
| with open(output_file, "w", encoding="utf-8") as f: | |
| for sample in collector.collect(datasets): | |
| f.write(json.dumps(sample, ensure_ascii=False) + "\n") | |
| count += 1 | |
| if count % 1000 == 0: | |
| logger.info(f" Collected {count} samples...") | |
| logger.info(f"✓ HuggingFace: {count} samples → {output_file}") | |
| return count | |
| def collect_arxiv(output_dir: str, max_queries: int = 5): | |
| """Collect papers từ arXiv.""" | |
| from nexus.data.collectors.arxiv_collector import ArxivCollector, CURATED_QUERIES | |
| collector = ArxivCollector() | |
| queries = CURATED_QUERIES[:max_queries] | |
| logger.info(f"Collecting arXiv papers ({len(queries)} queries)...") | |
| output_file = os.path.join(output_dir, "arxiv_papers.jsonl") | |
| count = 0 | |
| with open(output_file, "w", encoding="utf-8") as f: | |
| for sample in collector.collect(queries, max_per_query=20): | |
| f.write(json.dumps(sample, ensure_ascii=False) + "\n") | |
| count += 1 | |
| logger.info(f"✓ arXiv: {count} samples → {output_file}") | |
| return count | |
| def collect_wikipedia(output_dir: str, language: str = "vi"): | |
| """Collect articles từ Wikipedia.""" | |
| from nexus.data.collectors.wikipedia_collector import WikipediaCollector | |
| collector = WikipediaCollector(language=language) | |
| logger.info(f"Collecting Wikipedia ({language}) articles...") | |
| output_file = os.path.join(output_dir, f"wikipedia_{language}.jsonl") | |
| count = 0 | |
| with open(output_file, "w", encoding="utf-8") as f: | |
| for sample in collector.collect(): | |
| f.write(json.dumps(sample, ensure_ascii=False) + "\n") | |
| count += 1 | |
| logger.info(f"✓ Wikipedia ({language}): {count} samples → {output_file}") | |
| return count | |
| def collect_stackoverflow(output_dir: str, max_tags: int = 5, token: str = None): | |
| """Collect Q&A từ StackOverflow.""" | |
| from nexus.data.collectors.stackoverflow_collector import StackOverflowCollector, CURATED_TAGS | |
| collector = StackOverflowCollector(key=token) | |
| tags = CURATED_TAGS[:max_tags] | |
| logger.info(f"Collecting StackOverflow Q&A ({len(tags)} tags)...") | |
| output_file = os.path.join(output_dir, "stackoverflow.jsonl") | |
| count = 0 | |
| with open(output_file, "w", encoding="utf-8") as f: | |
| for sample in collector.collect(tags, max_per_tag=50): | |
| f.write(json.dumps(sample, ensure_ascii=False) + "\n") | |
| count += 1 | |
| logger.info(f"✓ StackOverflow: {count} samples → {output_file}") | |
| return count | |
| def main(): | |
| parser = argparse.ArgumentParser(description="Nexus Coder Data Collector") | |
| parser.add_argument( | |
| "--source", | |
| choices=["github", "huggingface", "arxiv", "wikipedia", "stackoverflow", "all"], | |
| default="all", | |
| help="Data source to collect from", | |
| ) | |
| parser.add_argument( | |
| "--output", | |
| type=str, | |
| default="./data/raw", | |
| help="Output directory", | |
| ) | |
| parser.add_argument("--max-repos", type=int, default=10, help="Max GitHub repos") | |
| parser.add_argument("--max-datasets", type=int, default=5, help="Max HF datasets") | |
| parser.add_argument("--max-queries", type=int, default=5, help="Max arXiv queries") | |
| parser.add_argument("--max-tags", type=int, default=5, help="Max SO tags") | |
| parser.add_argument("--language", type=str, default="vi", help="Wikipedia language") | |
| parser.add_argument("--github-token", type=str, default=os.environ.get("GITHUB_TOKEN")) | |
| parser.add_argument("--hf-token", type=str, default=os.environ.get("HF_TOKEN")) | |
| args = parser.parse_args() | |
| print("=" * 70) | |
| print(" NEXUS CODER v0.2 - DATA COLLECTOR") | |
| print(" Tác giả: Hieu Louis") | |
| print("=" * 70) | |
| os.makedirs(args.output, exist_ok=True) | |
| total = 0 | |
| if args.source in ("github", "all"): | |
| total += collect_github(args.output, args.max_repos, args.github_token) | |
| if args.source in ("huggingface", "all"): | |
| total += collect_huggingface(args.output, args.max_datasets, args.hf_token) | |
| if args.source in ("arxiv", "all"): | |
| total += collect_arxiv(args.output, args.max_queries) | |
| if args.source in ("wikipedia", "all"): | |
| total += collect_wikipedia(args.output, args.language) | |
| if args.source in ("stackoverflow", "all"): | |
| total += collect_stackoverflow(args.output, args.max_tags) | |
| print(f"\n{'=' * 70}") | |
| print(f" ✅ Total collected: {total} samples") | |
| print(f" 📁 Output: {args.output}") | |
| print(f"{'=' * 70}") | |
| print(f"\nNext step: Run scripts/prepare_dataset.py to process the raw data.") | |
| if __name__ == "__main__": | |
| main() | |