Instructions to use PraneethSunku/vic7b_sqlcoder7b_trial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PraneethSunku/vic7b_sqlcoder7b_trial with Transformers:

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

pipe = pipeline("text-generation", model="PraneethSunku/vic7b_sqlcoder7b_trial")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PraneethSunku/vic7b_sqlcoder7b_trial")
model = AutoModelForCausalLM.from_pretrained("PraneethSunku/vic7b_sqlcoder7b_trial")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PraneethSunku/vic7b_sqlcoder7b_trial with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PraneethSunku/vic7b_sqlcoder7b_trial"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PraneethSunku/vic7b_sqlcoder7b_trial",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/PraneethSunku/vic7b_sqlcoder7b_trial

SGLang

How to use PraneethSunku/vic7b_sqlcoder7b_trial 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 "PraneethSunku/vic7b_sqlcoder7b_trial" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PraneethSunku/vic7b_sqlcoder7b_trial",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "PraneethSunku/vic7b_sqlcoder7b_trial" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PraneethSunku/vic7b_sqlcoder7b_trial",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use PraneethSunku/vic7b_sqlcoder7b_trial with Docker Model Runner:
```
docker model run hf.co/PraneethSunku/vic7b_sqlcoder7b_trial
```

vic7b_sqlcoder7b_trial / README.md

PraneethSunku

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	---
	base_model:
	- lmsys/vicuna-7b-v1.5
	- defog/sqlcoder-7b-2
	library_name: transformers
	tags:
	- mergekit
	- merge

	---
	# merge

	This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

	## Merge Details
	### Merge Method

	This model was merged using the SLERP merge method.

	### Models Merged

	The following models were included in the merge:
	* [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5)
	* [defog/sqlcoder-7b-2](https://huggingface.co/defog/sqlcoder-7b-2)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	slices:
	- sources:
	- model: lmsys/vicuna-7b-v1.5
	layer_range:
	- 0
	- 32
	- model: defog/sqlcoder-7b-2
	layer_range:
	- 0
	- 32
	merge_method: slerp
	base_model: lmsys/vicuna-7b-v1.5
	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
	dtype: bfloat16
	```