Instructions to use kd13/Coder-o1-mini-reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kd13/Coder-o1-mini-reasoning with Transformers:

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

pipe = pipeline("text-generation", model="kd13/Coder-o1-mini-reasoning")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kd13/Coder-o1-mini-reasoning")
model = AutoModelForCausalLM.from_pretrained("kd13/Coder-o1-mini-reasoning")
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 kd13/Coder-o1-mini-reasoning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kd13/Coder-o1-mini-reasoning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kd13/Coder-o1-mini-reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kd13/Coder-o1-mini-reasoning

SGLang

How to use kd13/Coder-o1-mini-reasoning 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 "kd13/Coder-o1-mini-reasoning" \
    --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": "kd13/Coder-o1-mini-reasoning",
		"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 "kd13/Coder-o1-mini-reasoning" \
        --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": "kd13/Coder-o1-mini-reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kd13/Coder-o1-mini-reasoning with Docker Model Runner:
```
docker model run hf.co/kd13/Coder-o1-mini-reasoning
```

Coder-o1-mini-reasoning

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---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen2.5-Coder-1.5B
pipeline_tag: text-generation
library_name: transformers
tags:
- coder
- mini
- reasoning
- o1
---

# Coder-o1-mini-reasoning

A compact Python-focused reasoning model designed for coding assistance, debugging, code explanation, math reasoning, logic reasoning, Python concept explanation, and tool-style web search workflows.

The model is intended for lightweight assistant use cases where users need clear explanations, step-by-step reasoning, beginner-friendly Python help, and practical debugging support.

---

## Capabilities

This model can help with:

* Python coding assistance
* Python code explanation
* Python debugging and error fixing
* Python concept explanation
* Basic to intermediate competitive programming
* Math reasoning
* Logic reasoning
* Beginner-friendly programming guidance
* General chat
* Web search tool-call style conversations
* Multi-turn coding discussion

---

## Chat Format

The model follows a Harmony-style chat structure.

Supported interaction flow:

```text
system -> developer -> user -> reasoning -> tool call -> tool result -> final response
```

For normal chat or coding use, you can use a standard chat-template style prompt.

---

## Web Search Tool-Call Style

The model can be used in tool-calling style conversations where the assistant decides when search is needed, emits a tool call, receives a tool result, and then writes the final answer.

Example structure:

```text
system: You are a helpful assistant with access to web search.
user: Find the latest information about a topic.
assistant reasoning: Decide whether search is needed.
assistant tool call: search(...)
tool result: ...
assistant final: Answer using the search result.
```

Actual tool execution depends on your inference framework or application wrapper.

---

## Recommended Use Cases

This model is best suited for:

* Python learning assistants
* Coding tutor apps
* Debugging helpers
* Interview preparation
* Beginner-to-intermediate Python problem solving
* Math and logic explanation
* Lightweight reasoning chatbots
* Tool-call research experiments

---

## Limitations

This model is not recommended for:

* Very hard competitive programming problems
* Advanced game theory problems
* Complex graph theory or math-heavy algorithmic tasks
* Production-critical software generation without review
* Non-Python coding tasks such as C++, Java, Rust, Go, or JavaScript
* Security-sensitive code generation
* Medical, legal, or financial decision-making
* Long multi-file software engineering tasks

The model may sometimes:

* Produce incorrect reasoning
* Miss edge cases
* Over-explain simple problems
* Generate code that needs testing
* Struggle with very long context
* Use tool-call format inconsistently depending on the prompt

Always test generated code before using it.

---

## License

Please check the model repository license before commercial or production use.

---

## Disclaimer

This model is an experimental small reasoning and coding assistant. It should be used as a helpful assistant, not as a guaranteed source of truth. For important tasks, verify outputs with tests, documentation, and human review.