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 9 days ago

Commit

33f3474

verified ·

1 Parent(s): a4353ce

Update chat_template.jinja

Browse files

Files changed (1) hide show

chat_template.jinja +18 -14

chat_template.jinja CHANGED Viewed

@@ -1,26 +1,30 @@
 {% if not add_generation_prompt is defined %}
-{% set add_generation_prompt = false %}
 {% endif %}
 {%- set ns = namespace(found=false) -%}
 {%- for message in messages -%}
-    {%- if message['role'] == 'system' -%}
-        {%- set ns.found = true -%}
-    {%- endif -%}
 {%- endfor -%}
-{{bos_token}}{%- if not ns.found -%}
-{{'You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\n'}}
 {%- endif %}
 {%- for message in messages %}
-    {%- if message['role'] == 'system' %}
-{{ message['content'] }}
     {%- else %}
-        {%- if message['role'] == 'user' %}
-{{'### Instruction:\n' + message['content'] + '\n'}}
-        {%- else %}
-{{'### Response:\n' + message['content'] + '\n<|EOT|>\n'}}
-        {%- endif %}
     {%- endif %}
 {%- endfor %}
 {% if add_generation_prompt %}
-{{'### Response:'}}
 {% endif %}

 {% if not add_generation_prompt is defined %}
+  {% set add_generation_prompt = false %}
 {% endif %}
 {%- set ns = namespace(found=false) -%}
 {%- for message in messages -%}
+  {%- if message['role'] == 'system' -%}
+    {%- set ns.found = true -%}
+  {%- endif -%}
 {%- endfor -%}
+{{ bos_token }}{%- if not ns.found -%}
+  {{ 'You are Neura Debugger, an expert AI debugging assistant specialized in finding, analyzing, and fixing code errors. Your primary goal is to help developers identify bugs, understand root causes, and implement correct solutions. You ONLY respond to programming, debugging, software development, and computer science related queries. For any non-technical questions, political topics, security vulnerabilities exploitation (as opposed to fixing them), or off-topic discussions, you will politely refuse and redirect to debugging topics.\n\nWhen debugging:\n1. First identify the error type (syntax, runtime, logic, or performance)\n2. Explain the root cause clearly\n3. Provide the corrected code\n4. Suggest preventive measures\n5. Use a step-by-step analytical approach\n\nYou prioritize clarity, accuracy, and educational value in all responses.\n' }}
 {%- endif %}
 {%- for message in messages %}
+  {%- if message['role'] == 'system' %}
+    {{ message['content'] }}
+  {%- else %}
+    {%- if message['role'] == 'user' %}
+      {{ '### Debug Request:\n' + message['content'] + '\n' }}
     {%- else %}
+      {{ '### Debug Analysis:\n' + message['content'] + '\n###\n' }}
     {%- endif %}
+  {%- endif %}
 {%- endfor %}
 {% if add_generation_prompt %}
+  {{ '### Debug Analysis:' }}
 {% endif %}