Instructions to use NotShrirang/DeepSeek-R1-Distill-Qwen-1.5B-SQL-Coder-PEFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NotShrirang/DeepSeek-R1-Distill-Qwen-1.5B-SQL-Coder-PEFT with Transformers:

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

pipe = pipeline("text-generation", model="NotShrirang/DeepSeek-R1-Distill-Qwen-1.5B-SQL-Coder-PEFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("NotShrirang/DeepSeek-R1-Distill-Qwen-1.5B-SQL-Coder-PEFT", device_map="auto")

PEFT
How to use NotShrirang/DeepSeek-R1-Distill-Qwen-1.5B-SQL-Coder-PEFT with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NotShrirang/DeepSeek-R1-Distill-Qwen-1.5B-SQL-Coder-PEFT with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/NotShrirang/DeepSeek-R1-Distill-Qwen-1.5B-SQL-Coder-PEFT

SGLang

How to use NotShrirang/DeepSeek-R1-Distill-Qwen-1.5B-SQL-Coder-PEFT 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 "NotShrirang/DeepSeek-R1-Distill-Qwen-1.5B-SQL-Coder-PEFT" \
    --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": "NotShrirang/DeepSeek-R1-Distill-Qwen-1.5B-SQL-Coder-PEFT",
		"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 "NotShrirang/DeepSeek-R1-Distill-Qwen-1.5B-SQL-Coder-PEFT" \
        --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": "NotShrirang/DeepSeek-R1-Distill-Qwen-1.5B-SQL-Coder-PEFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NotShrirang/DeepSeek-R1-Distill-Qwen-1.5B-SQL-Coder-PEFT with Docker Model Runner:
```
docker model run hf.co/NotShrirang/DeepSeek-R1-Distill-Qwen-1.5B-SQL-Coder-PEFT
```

NotShrirang commited on Feb 24, 2025

Commit

80d2487

verified ·

1 Parent(s): 60d4c28

Update adapter_config.json

Browse files

Files changed (1) hide show

adapter_config.json +2 -0

adapter_config.json CHANGED Viewed

@@ -1,11 +1,13 @@
 {
   "alpha_pattern": {},
   "auto_mapping": null,
   "base_model_name_or_path": "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
   "bias": "none",
   "eva_config": null,
   "exclude_modules": null,
   "fan_in_fan_out": false,
   "inference_mode": true,
   "init_lora_weights": true,
   "layer_replication": null,

 {
   "alpha_pattern": {},
+  "auto_class": "PeftModelForCausalLM",
   "auto_mapping": null,
   "base_model_name_or_path": "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
   "bias": "none",
   "eva_config": null,
   "exclude_modules": null,
   "fan_in_fan_out": false,
+  "inference_class": "AutoPeftModelForCausalLM",
   "inference_mode": true,
   "init_lora_weights": true,
   "layer_replication": null,