Instructions to use quin210/vitutor-deepseek-r1-8b-sft-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use quin210/vitutor-deepseek-r1-8b-sft-full with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/DeepSeek-R1-Distill-Llama-8B")
model = PeftModel.from_pretrained(base_model, "quin210/vitutor-deepseek-r1-8b-sft-full")

Transformers

How to use quin210/vitutor-deepseek-r1-8b-sft-full with Transformers:

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

pipe = pipeline("text-generation", model="quin210/vitutor-deepseek-r1-8b-sft-full")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("quin210/vitutor-deepseek-r1-8b-sft-full", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use quin210/vitutor-deepseek-r1-8b-sft-full with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "quin210/vitutor-deepseek-r1-8b-sft-full"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "quin210/vitutor-deepseek-r1-8b-sft-full",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/quin210/vitutor-deepseek-r1-8b-sft-full

SGLang

How to use quin210/vitutor-deepseek-r1-8b-sft-full 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 "quin210/vitutor-deepseek-r1-8b-sft-full" \
    --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": "quin210/vitutor-deepseek-r1-8b-sft-full",
		"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 "quin210/vitutor-deepseek-r1-8b-sft-full" \
        --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": "quin210/vitutor-deepseek-r1-8b-sft-full",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use quin210/vitutor-deepseek-r1-8b-sft-full with Docker Model Runner:
```
docker model run hf.co/quin210/vitutor-deepseek-r1-8b-sft-full
```

vitutor-deepseek-r1-8b-sft-full / best_metric.json

quin210

best ckpt by dev MCQ acc (epoch 0.50, acc 0.0979)

409d61e verified 4 months ago

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344 Bytes

	{
	"epoch": 0.5002785992885003,
	"dev_accuracy": 0.09787234042553192,
	"history": [
	{
	"epoch": 0.4004971925764005,
	"accuracy": 0.08085106382978724,
	"elapsed_sec": 2866.9569416046143
	},
	{
	"epoch": 0.5002785992885003,
	"accuracy": 0.09787234042553192,
	"elapsed_sec": 3098.443959236145
	}
	]
	}