Coder-o1-mini-reasoning/README.md

---
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.

---

## Basic Usage

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

MODEL_PATH = "kd13/Coder-o1-mini-reasoning"

tok = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_PATH,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)
model.eval()

if tok.pad_token is None:
    tok.pad_token = tok.eos_token

IM_END_ID = tok.convert_tokens_to_ids("<|im_end|>")
if IM_END_ID is None or IM_END_ID == tok.unk_token_id:
    IM_END_ID = tok.eos_token_id
```

---

## 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.