Instructions to use neuracoder/neuradebugger-Micro-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use neuracoder/neuradebugger-Micro-1b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="neuracoder/neuradebugger-Micro-1b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("neuracoder/neuradebugger-Micro-1b")
model = AutoModelForCausalLM.from_pretrained("neuracoder/neuradebugger-Micro-1b")
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 neuracoder/neuradebugger-Micro-1b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "neuracoder/neuradebugger-Micro-1b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "neuracoder/neuradebugger-Micro-1b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/neuracoder/neuradebugger-Micro-1b

SGLang

How to use neuracoder/neuradebugger-Micro-1b 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 "neuracoder/neuradebugger-Micro-1b" \
    --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": "neuracoder/neuradebugger-Micro-1b",
		"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 "neuracoder/neuradebugger-Micro-1b" \
        --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": "neuracoder/neuradebugger-Micro-1b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use neuracoder/neuradebugger-Micro-1b with Docker Model Runner:
```
docker model run hf.co/neuracoder/neuradebugger-Micro-1b
```

neuracoder commited on 5 days ago

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@@ -35,6 +35,8 @@ metrics:
 Unlike general code generation models that often produce new bugs, NeuraDebugger-Micro focuses exclusively on **finding and fixing errors** in existing code. It understands exception traces, logical flaws, edge cases, and common pitfalls across 12 programming languages. Despite its tiny size, it runs on laptops, CPU‑only machines, and even Raspberry Pi, giving every developer an expert debugger at their fingertips.
 ---
 ## ✨ Key Features (Detailed)
@@ -70,7 +72,6 @@ Unlike general code generation models that often produce new bugs, NeuraDebugger
 ---
 ## 📊 Benchmarks & Comprehensive Evaluation
-![Neuracoder Tiny](https://huggingface.co/neuracoder/neuradebugger-Micro-1.1b/resolve/main/NeuraDebugger-Micro-1.1B.png)
 We evaluated NeuraDebugger-Micro on three specialised debugging datasets:
 1. **Defects4J (Java)** – 835 real bugs from 17 real‑world projects (Apache Commons, JFreeChart, etc.).

 Unlike general code generation models that often produce new bugs, NeuraDebugger-Micro focuses exclusively on **finding and fixing errors** in existing code. It understands exception traces, logical flaws, edge cases, and common pitfalls across 12 programming languages. Despite its tiny size, it runs on laptops, CPU‑only machines, and even Raspberry Pi, giving every developer an expert debugger at their fingertips.
+![Neuracoder Tiny](https://huggingface.co/neuracoder/neuradebugger-Micro-1.1b/resolve/main/NeuraDebugger-Micro-1.1B.png)
 ---
 ## ✨ Key Features (Detailed)
 ---
 ## 📊 Benchmarks & Comprehensive Evaluation
 We evaluated NeuraDebugger-Micro on three specialised debugging datasets:
 1. **Defects4J (Java)** – 835 real bugs from 17 real‑world projects (Apache Commons, JFreeChart, etc.).