Instructions to use N11100/deepseek-r1-python-code-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use N11100/deepseek-r1-python-code-ft with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B")
model = PeftModel.from_pretrained(base_model, "N11100/deepseek-r1-python-code-ft")

Transformers

How to use N11100/deepseek-r1-python-code-ft with Transformers:

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

pipe = pipeline("text-generation", model="N11100/deepseek-r1-python-code-ft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("N11100/deepseek-r1-python-code-ft", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use N11100/deepseek-r1-python-code-ft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "N11100/deepseek-r1-python-code-ft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "N11100/deepseek-r1-python-code-ft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/N11100/deepseek-r1-python-code-ft

SGLang

How to use N11100/deepseek-r1-python-code-ft 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 "N11100/deepseek-r1-python-code-ft" \
    --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": "N11100/deepseek-r1-python-code-ft",
		"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 "N11100/deepseek-r1-python-code-ft" \
        --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": "N11100/deepseek-r1-python-code-ft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use N11100/deepseek-r1-python-code-ft with Docker Model Runner:
```
docker model run hf.co/N11100/deepseek-r1-python-code-ft
```

deepseek-r1-python-code-ft / README.md

N11100

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	---
	library_name: peft
	license: mit
	base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
	tags:
	- base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
	- lora
	- transformers
	pipeline_tag: text-generation
	model-index:
	- name: deepseek-r1-python-code-ft
	results: []
	thumbnail: https://huggingface.co/N11100/deepseek-r1-python-code-ft/resolve/main/thumbnail.png
	github: https://github.com/hubgunter4-ops/deepseek-r1-python-code-ft
	inference: true
	widget:
	- text: "Explain how to secure a linux server."
	example_title: "Security Best Practices"
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# deepseek-r1-python-code-ft

	This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) on an unknown dataset.

	## Model description

	More information needed

	## Intended uses & limitations

	More information needed

	## Training and evaluation data

	More information needed

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 0.0002
	- train_batch_size: 4
	- eval_batch_size: 8
	- seed: 42
	- gradient_accumulation_steps: 4
	- total_train_batch_size: 16
	- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
	- lr_scheduler_type: linear
	- num_epochs: 1
	- mixed_precision_training: Native AMP

	### Training results



	### Framework versions

	- PEFT 0.19.1
	- Transformers 5.9.0
	- Pytorch 2.11.0+cu128
	- Datasets 4.0.0
	- Tokenizers 0.22.2