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
| """Crypto Tools - hashing, encryption.""" | |
| from __future__ import annotations | |
| import hashlib | |
| import hmac | |
| import base64 | |
| from typing import Dict, Any | |
| from .base import Tool, ToolResult, ToolContext, ToolCategory, ToolSafety | |
| class HashTool(Tool): | |
| """Compute hash của data/file.""" | |
| category = ToolCategory.CRYPTO | |
| safety = ToolSafety.SAFE | |
| def name(self) -> str: | |
| return "hash" | |
| def description(self) -> str: | |
| return "Compute hash (md5, sha1, sha256, sha512, blake2) của string hoặc file." | |
| def parameters(self) -> Dict[str, Any]: | |
| return { | |
| "type": "object", | |
| "properties": { | |
| "input": {"type": "string", "description": "String or file path"}, | |
| "algorithm": {"type": "string", "enum": ["md5", "sha1", "sha256", "sha512", "blake2b", "blake2s"], "default": "sha256"}, | |
| "is_file": {"type": "boolean", "default": False}, | |
| }, | |
| "required": ["input"], | |
| } | |
| def execute(self, args: Dict[str, Any], context: ToolContext) -> ToolResult: | |
| import os | |
| inp = args["input"] | |
| algorithm = args.get("algorithm", "sha256") | |
| is_file = args.get("is_file", False) | |
| try: | |
| h = hashlib.new(algorithm) | |
| if is_file or (os.path.exists(inp) and len(inp) < 1024): | |
| with open(inp, "rb") as f: | |
| for chunk in iter(lambda: f.read(8192), b""): | |
| h.update(chunk) | |
| source = f"file:{inp}" | |
| else: | |
| h.update(inp.encode("utf-8")) | |
| source = "string" | |
| return ToolResult( | |
| success=True, | |
| output=f"{algorithm}({source}) = {h.hexdigest()}", | |
| metadata={ | |
| "algorithm": algorithm, | |
| "hash": h.hexdigest(), | |
| "source": source, | |
| }, | |
| ) | |
| except Exception as e: | |
| return ToolResult(success=False, error=str(e), return_code=1) | |
| class EncryptTool(Tool): | |
| """Encrypt/decrypt data với AES (requires cryptography lib).""" | |
| category = ToolCategory.CRYPTO | |
| safety = ToolSafety.DANGEROUS | |
| def name(self) -> str: | |
| return "encrypt" | |
| def description(self) -> str: | |
| return "Encrypt/decrypt data với AES-256-GCM. Requires 'cryptography' lib." | |
| def parameters(self) -> Dict[str, Any]: | |
| return { | |
| "type": "object", | |
| "properties": { | |
| "action": {"type": "string", "enum": ["encrypt", "decrypt"]}, | |
| "data": {"type": "string"}, | |
| "password": {"type": "string"}, | |
| }, | |
| "required": ["action", "data", "password"], | |
| } | |
| def execute(self, args: Dict[str, Any], context: ToolContext) -> ToolResult: | |
| try: | |
| from cryptography.hazmat.primitives.ciphers.aead import AESGCM | |
| from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC | |
| from cryptography.hazmat.primitives import hashes | |
| import os as _os | |
| except ImportError: | |
| return ToolResult( | |
| success=False, | |
| error="cryptography not installed. Run: pip install cryptography", | |
| return_code=1, | |
| ) | |
| action = args["action"] | |
| data = args["data"] | |
| password = args["password"] | |
| try: | |
| if action == "encrypt": | |
| salt = _os.urandom(16) | |
| kdf = PBKDF2HMAC( | |
| algorithm=hashes.SHA256(), | |
| length=32, | |
| salt=salt, | |
| iterations=100000, | |
| ) | |
| key = kdf.derive(password.encode()) | |
| aesgcm = AESGCM(key) | |
| nonce = _os.urandom(12) | |
| ciphertext = aesgcm.encrypt(nonce, data.encode(), None) | |
| encrypted = base64.b64encode(salt + nonce + ciphertext).decode() | |
| return ToolResult( | |
| success=True, | |
| output=encrypted, | |
| metadata={"action": "encrypt", "algorithm": "AES-256-GCM"}, | |
| ) | |
| else: # decrypt | |
| raw = base64.b64decode(data) | |
| salt, nonce, ciphertext = raw[:16], raw[16:28], raw[28:] | |
| kdf = PBKDF2HMAC( | |
| algorithm=hashes.SHA256(), | |
| length=32, | |
| salt=salt, | |
| iterations=100000, | |
| ) | |
| key = kdf.derive(password.encode()) | |
| aesgcm = AESGCM(key) | |
| plaintext = aesgcm.decrypt(nonce, ciphertext, None).decode() | |
| return ToolResult( | |
| success=True, | |
| output=plaintext, | |
| metadata={"action": "decrypt"}, | |
| ) | |
| except Exception as e: | |
| return ToolResult(success=False, error=str(e), return_code=1) | |