Instructions to use Adnan2942/DeepSeek-R1-1.5B-LoRA-Adapter-Smol with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Adnan2942/DeepSeek-R1-1.5B-LoRA-Adapter-Smol with Transformers:

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

pipe = pipeline("text-generation", model="Adnan2942/DeepSeek-R1-1.5B-LoRA-Adapter-Smol")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Adnan2942/DeepSeek-R1-1.5B-LoRA-Adapter-Smol", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Adnan2942/DeepSeek-R1-1.5B-LoRA-Adapter-Smol with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Adnan2942/DeepSeek-R1-1.5B-LoRA-Adapter-Smol"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Adnan2942/DeepSeek-R1-1.5B-LoRA-Adapter-Smol",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Adnan2942/DeepSeek-R1-1.5B-LoRA-Adapter-Smol

SGLang

How to use Adnan2942/DeepSeek-R1-1.5B-LoRA-Adapter-Smol 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 "Adnan2942/DeepSeek-R1-1.5B-LoRA-Adapter-Smol" \
    --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": "Adnan2942/DeepSeek-R1-1.5B-LoRA-Adapter-Smol",
		"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 "Adnan2942/DeepSeek-R1-1.5B-LoRA-Adapter-Smol" \
        --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": "Adnan2942/DeepSeek-R1-1.5B-LoRA-Adapter-Smol",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Adnan2942/DeepSeek-R1-1.5B-LoRA-Adapter-Smol with Docker Model Runner:
```
docker model run hf.co/Adnan2942/DeepSeek-R1-1.5B-LoRA-Adapter-Smol
```

Model Card for DeepSeek-R1-Distill-Qwen-1.5B-LoRA-Adapter

This repo contains the adapter weights of a fine-tuned version of deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B. It has been trained using TRL.It's fine-tuned on HuggingFaceTB/smoltalk "self-oss-instruct" Dataset.

This is the fine-tuned model merged with the adapter weights: https://huggingface.co/Adnan2942/DeepSeek-R1-1.5B-LoRA-Smol

The target Modules were:["q_proj","v_proj","k_proj","o_proj"].

Training procedure

Framework versions

TRL: 1.13.0
Transformers: 5.0.0
Pytorch: 2.10.0+cu128
Datasets: 5.0.0
Tokenizers: 0.22.2

Citations

Cite TRL as:

@software{vonwerra2020trl,
  title   = {{TRL: Transformers Reinforcement Learning}},
  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
  license = {Apache-2.0},
  url     = {https://github.com/huggingface/trl},
  year    = {2020}
}

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Model tree for Adnan2942/DeepSeek-R1-1.5B-LoRA-Adapter-Smol

Base model

deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B

Finetuned

(859)

this model

Adnan2942
/

DeepSeek-R1-1.5B-LoRA-Adapter-Smol

Model Card for DeepSeek-R1-Distill-Qwen-1.5B-LoRA-Adapter

Training procedure

Framework versions

Citations

Model tree for Adnan2942/DeepSeek-R1-1.5B-LoRA-Adapter-Smol

Dataset used to train Adnan2942/DeepSeek-R1-1.5B-LoRA-Adapter-Smol