Instructions to use WontonLoodle/sqlcoderseaLLM-7B-slerp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WontonLoodle/sqlcoderseaLLM-7B-slerp with Transformers:

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

pipe = pipeline("text-generation", model="WontonLoodle/sqlcoderseaLLM-7B-slerp")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("WontonLoodle/sqlcoderseaLLM-7B-slerp")
model = AutoModelForCausalLM.from_pretrained("WontonLoodle/sqlcoderseaLLM-7B-slerp")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use WontonLoodle/sqlcoderseaLLM-7B-slerp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WontonLoodle/sqlcoderseaLLM-7B-slerp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WontonLoodle/sqlcoderseaLLM-7B-slerp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/WontonLoodle/sqlcoderseaLLM-7B-slerp

SGLang

How to use WontonLoodle/sqlcoderseaLLM-7B-slerp 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 "WontonLoodle/sqlcoderseaLLM-7B-slerp" \
    --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": "WontonLoodle/sqlcoderseaLLM-7B-slerp",
		"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 "WontonLoodle/sqlcoderseaLLM-7B-slerp" \
        --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": "WontonLoodle/sqlcoderseaLLM-7B-slerp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use WontonLoodle/sqlcoderseaLLM-7B-slerp with Docker Model Runner:
```
docker model run hf.co/WontonLoodle/sqlcoderseaLLM-7B-slerp
```

sqlcoderseaLLM-7B-slerp

sqlcoderseaLLM-7B-slerp is a merge of the following models using mergekit:

Configuration

```yaml slices:

sources:
- model: defog/sqlcoder-7b layer_range: [0, 32]
- model: SeaLLMs/SeaLLM-7B-v2 layer_range: [0, 32]

merge_method: slerp base_model: SeaLLMs/SeaLLM-7B-v2 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 # fallback for rest of tensors dtype: float16 ```

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