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
```

kd13 commited on 2 days ago

Commit

757b8d7

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1 Parent(s): fbbbed5

Update README.md

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@@ -53,33 +53,6 @@ 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.

 ---
 ## 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.