Instructions to use kirankotha/mistral7b-sql-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kirankotha/mistral7b-sql-model with Transformers:

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

pipe = pipeline("text-generation", model="kirankotha/mistral7b-sql-model")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kirankotha/mistral7b-sql-model")
model = AutoModelForCausalLM.from_pretrained("kirankotha/mistral7b-sql-model")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use kirankotha/mistral7b-sql-model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kirankotha/mistral7b-sql-model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kirankotha/mistral7b-sql-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kirankotha/mistral7b-sql-model

SGLang

How to use kirankotha/mistral7b-sql-model 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 "kirankotha/mistral7b-sql-model" \
    --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": "kirankotha/mistral7b-sql-model",
		"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 "kirankotha/mistral7b-sql-model" \
        --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": "kirankotha/mistral7b-sql-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kirankotha/mistral7b-sql-model with Docker Model Runner:
```
docker model run hf.co/kirankotha/mistral7b-sql-model
```

Mistral-7B SQL (fine-tuned)

Fine-tuned Mistral-7B for Text-to-SQL on b-mc2/sql-create-context.

Usage (Transformers)

from transformers import AutoTokenizer, AutoModelForCausalLM import torch

model_id = "kirankotha/mistral7b-sql-model" tok = AutoTokenizer.from_pretrained(model_id) mdl = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.float16)

prompt = ( "You are a text-to-SQL model. " "### Input: " "Which product has the highest price? " "### Context: " "CREATE TABLE products (id INTEGER, name TEXT, price REAL) " "### Response: " )

ids = tok(prompt, return_tensors="pt").to(mdl.device) out = mdl.generate(**ids, max_new_tokens=100, do_sample=False, pad_token_id=tok.pad_token_id) print(tok.decode(out[0], skip_special_tokens=True))

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Safetensors

Model size

7B params

Tensor type

F32

Model tree for kirankotha/mistral7b-sql-model

Base model

mistralai/Mistral-7B-v0.1

Finetuned

(1033)

this model

kirankotha
/

mistral7b-sql-model

Mistral-7B SQL (fine-tuned)

Usage (Transformers)

Model tree for kirankotha/mistral7b-sql-model

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