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: 2,130 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 | """Debugging Skill - Debug và fix lỗi code."""
from __future__ import annotations
from typing import List
from .base import Skill, SkillResult, SkillContext, SkillCategory, SkillPriority
class DebuggingSkill(Skill):
"""Debug code: phân tích stack trace, tìm root cause, suggest fix."""
category = SkillCategory.CODE
priority = SkillPriority.CRITICAL
keywords: List[str] = [
"debug", "lỗi", "error", "exception", "traceback",
"stack trace", "fix", "sửa", "khắc phục", "crash",
"fail", "không chạy", "broken", "không hoạt động",
]
@property
def name(self) -> str:
return "debugging"
@property
def description(self) -> str:
return (
"Debug code: parse stack traces, identify root cause, "
"suggest minimal fix, verify fix doesn't break other code."
)
def execute(self, context: SkillContext) -> SkillResult:
debug_steps = [
"1. Reproduce the error consistently",
"2. Parse stack trace / error message",
"3. Identify root cause (not symptom)",
"4. Propose minimal fix",
"5. Check for related issues (same pattern elsewhere)",
"6. Suggest regression test to prevent recurrence",
"7. Verify fix doesn't introduce new bugs",
]
return SkillResult(
success=True,
output=f"[Debugging] Following {len(debug_steps)}-step debugging protocol.",
metadata={
"skill": self.name,
"debug_steps": debug_steps,
"supports": [
"Python traceback", "JavaScript console errors",
"Java stack traces", "Go panics", "Rust panics",
"C++ segfaults", "Ruby exceptions",
],
},
suggestions=[
"Provide full stack trace for accurate diagnosis",
"Include input that triggered the bug",
"Mention recent changes that may have introduced the bug",
],
)
|