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", dtype="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

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 25, 2025

Commit

6a279c0

verified ·

1 Parent(s): a92cdb3

Update README.md

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@@ -2,6 +2,9 @@
 library_name: transformers
 tags:
 - code
 license: apache-2.0
 datasets:
 - gretelai/synthetic_text_to_sql
@@ -38,11 +41,12 @@ Due to its lightweight architecture, this model can be deployed efficiently on l
 You can load the model using 🤗 Transformers:
 ```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-model_name = "NotShrirang/sql-deepseek-r1-distill-qwen-1.5B"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
 prompt = "Write a SQL query to get the total revenue from the sales table."
 inputs = tokenizer(prompt, return_tensors="pt")

 library_name: transformers
 tags:
 - code
+- peft
+- sql-generation
+- text-generation-inference
 license: apache-2.0
 datasets:
 - gretelai/synthetic_text_to_sql
 You can load the model using 🤗 Transformers:
 ```python
+from peft import AutoPeftModelForCausalLM
+from transformers import AutoTokenizer
+import torch
+model = AutoPeftModelForCausalLM.from_pretrained("NotShrirang/DeepSeek-R1-Distill-Qwen-1.5B-SQL-Coder-PEFT")
+tokenizer = AutoTokenizer.from_pretrained("NotShrirang/DeepSeek-R1-Distill-Qwen-1.5B-SQL-Coder-PEFT")
 prompt = "Write a SQL query to get the total revenue from the sales table."
 inputs = tokenizer(prompt, return_tensors="pt")