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

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
```

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

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

Downloads last month: 2

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for PraneethSunku/vic7b_sqlcoder7b_trial

defog/sqlcoder-7b-2

lmsys/vicuna-7b-v1.5

Merge model

this model