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

Libraries

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

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

pipe = pipeline("text-generation", model="kd13/Coder-o1-mini-reasoning-AWQ")
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-AWQ")
model = AutoModelForCausalLM.from_pretrained("kd13/Coder-o1-mini-reasoning-AWQ", device_map="auto")
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-AWQ 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-AWQ"
# 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-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

Coder-o1-mini-reasoning-AWQ / README.md

kd13

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	---
	base_model: kd13/Coder-o1-mini-reasoning
	library_name: transformers
	pipeline_tag: text-generation
	license: mit
	language:
	- en
	tags:
	- awq
	- w4a16
	- compressed-tensors
	- quantized
	- vllm
	- code
	---

	# Coder-o1-mini-reasoning - AWQ

	4-bit AWQ quantization of [kd13/Coder-o1-mini-reasoning](https://huggingface.co/kd13/Coder-o1-mini-reasoning), a compact Python-focused reasoning model for coding assistance, debugging, code explanation, and math/logic reasoning.

	Quantized with [llm-compressor](https://github.com/vllm-project/llm-compressor) using `AWQModifier` + `W4A16_ASYM`. Calibrated on 256 code-instruction samples at 2048 tokens, with the model's own chat template applied.

	`lm_head` is left at full precision. Weights are 4-bit; activations stay 16-bit.

	## Format

	This is compressed-tensors format, which is what current AWQ tooling produces. vLLM and transformers both detect it automatically from `config.json` — you do not need to pass `--quantization awq`. The older AutoAWQ format is not interchangeable with this one; if a loader expects `quant_config.json`, it wants the legacy format and will not read this repo.

	## Usage

	### vLLM

	```bash
	vllm serve kd13/Coder-o1-mini-reasoning-AWQ --max-model-len 8192
	```

	### Transformers

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model = AutoModelForCausalLM.from_pretrained("kd13/Coder-o1-mini-reasoning-AWQ", device_map="auto")
	tok = AutoTokenizer.from_pretrained("kd13/Coder-o1-mini-reasoning-AWQ")

	msgs = [
	{"role": "system", "content": "You are a helpful Python coding assistant."},
	{"role": "user", "content": "Explain list comprehensions with an example."},
	]
	prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
	ids = tok(prompt, return_tensors="pt").to(model.device)
	print(tok.decode(model.generate(**ids, max_new_tokens=300)[0]))
	```

	Requires `pip install compressed-tensors`.

	## Hardware

	A CUDA GPU is required — AWQ has no CPU path. For local or CPU inference use the GGUF build instead.

	On Ampere or newer (compute capability 8.0+) vLLM uses the Marlin kernel, which is where the throughput gains come from. Turing cards such as the T4 fall back to a slower kernel and see much less benefit.

	## Chat template

	ChatML, with Qwen-style tool calling:

	```
	<\|im_start\|>system
	{system}<\|im_end\|>
	<\|im_start\|>user
	{message}<\|im_end\|>
	<\|im_start\|>assistant
	```

	Tool definitions are injected into the system message inside `<tools>` tags, and the model replies with a JSON object inside `<tool_call>` tags. Tool results are returned wrapped in `<tool_response>`. vLLM exposes this through its OpenAI-compatible `tools` parameter.

	A default system prompt is applied when you do not supply one. Pass an explicit system prompt to control the assistant's stated identity.