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Model: kd13/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.
---
## 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.

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are MUA o1 Pro, created by OumuamuaAI. You are a helpful assistant.' }}
{%- endif %}
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0]['role'] == 'system' %}
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- else %}
{{- '<|im_start|>system\nYou are MUA o1 Pro, created by OumuamuaAI. You are a helpful assistant.<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role }}
{%- if message.content %}
{{- '\n' + message.content }}
{%- endif %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '\n<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{{- tool_call.arguments | tojson }}
{{- '}\n</tool_call>' }}
{%- endfor %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}

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