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

Libraries

How to use kd13/Type-o1-mini-instruct with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kd13/Type-o1-mini-instruct"
# 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/Type-o1-mini-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kd13/Type-o1-mini-instruct

SGLang

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

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

Type-o1-mini-instruct

A compact general-purpose instruct model designed for everyday assistant use across a wide range of domains — from science and math to writing, coding, language tasks, and tool-style web search workflows.

The model is intended for lightweight assistant use cases where users need clear, well-structured answers, helpful explanations, and practical support across many subject areas.

Capabilities

This model can help with:

General chat and multi-turn conversation
Biology, chemistry, and physics questions and explanations
Mathematics and quantitative reasoning
Engineering concepts and explanations
Health and medical information (general, non-clinical)
Python coding assistance and code explanation
Creative writing (stories, poetry, writing prompts)
Content generation (marketing copy, social media captions, emails)
English grammar correction and rewriting
Advanced NLP tasks:
- Fill-mask
- Table question answering
- Context-based question answering (SQuAD style)
- Summarization (dialogue, news, and scientific papers)
English ↔ Hindi translation
School and coursework-level question answering
Web search tool-call style conversations

Chat Format

The model follows a Harmony-style chat structure.

Supported interaction flow:

system -> developer -> user -> tool call -> tool result -> final response

For normal chat 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 a search is needed, emits a tool call, receives a tool result, and then writes the final answer.

Example structure:

system: You are a helpful assistant with access to web search.
user: Find the latest information about a topic.
assistant tool call: web_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:

General-purpose lightweight assistants
Study and homework helpers across science subjects
Writing and content generation helpers
Grammar and language correction tools
English ↔ Hindi translation helpers
Summarization and document Q&A tools
Beginner Python learning assistants
Tool-call research experiments
Chatbots that need broad domain coverage in a small model

Limitations

This model is not recommended for:

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
Advanced research-level science or mathematics
Long multi-file software engineering tasks
Tasks requiring very long context
High-stakes factual lookup without verification

The model may sometimes:

Produce incorrect facts or reasoning
Miss edge cases
Over-explain simple questions
Generate code that needs testing
Struggle with very long context
Use tool-call format inconsistently depending on the prompt
Give uneven quality across its many supported domains

Always verify important outputs and 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 general-purpose 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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Safetensors

Model size

1B params

Tensor type

BF16

Model tree for kd13/Type-o1-mini-instruct

Base model

meta-llama/Llama-3.2-1B

Finetuned

(937)

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