Instructions to use pavankumarbalijepalli/phi2-sqlcoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pavankumarbalijepalli/phi2-sqlcoder with Transformers:

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

pipe = pipeline("text-generation", model="pavankumarbalijepalli/phi2-sqlcoder", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pavankumarbalijepalli/phi2-sqlcoder", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("pavankumarbalijepalli/phi2-sqlcoder", trust_remote_code=True)

PEFT
How to use pavankumarbalijepalli/phi2-sqlcoder with PEFT:
```
Task type is invalid.
```

llama-cpp-python

How to use pavankumarbalijepalli/phi2-sqlcoder with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="pavankumarbalijepalli/phi2-sqlcoder",
	filename="phi2_sqlcoder_f16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Inference
Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use pavankumarbalijepalli/phi2-sqlcoder with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf pavankumarbalijepalli/phi2-sqlcoder:F16
# Run inference directly in the terminal:
llama-cli -hf pavankumarbalijepalli/phi2-sqlcoder:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf pavankumarbalijepalli/phi2-sqlcoder:F16
# Run inference directly in the terminal:
llama-cli -hf pavankumarbalijepalli/phi2-sqlcoder:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf pavankumarbalijepalli/phi2-sqlcoder:F16
# Run inference directly in the terminal:
./llama-cli -hf pavankumarbalijepalli/phi2-sqlcoder:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf pavankumarbalijepalli/phi2-sqlcoder:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pavankumarbalijepalli/phi2-sqlcoder:F16

Use Docker

docker model run hf.co/pavankumarbalijepalli/phi2-sqlcoder:F16

LM Studio
Jan

vLLM

How to use pavankumarbalijepalli/phi2-sqlcoder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pavankumarbalijepalli/phi2-sqlcoder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pavankumarbalijepalli/phi2-sqlcoder",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pavankumarbalijepalli/phi2-sqlcoder:F16

SGLang

How to use pavankumarbalijepalli/phi2-sqlcoder 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 "pavankumarbalijepalli/phi2-sqlcoder" \
    --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": "pavankumarbalijepalli/phi2-sqlcoder",
		"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 "pavankumarbalijepalli/phi2-sqlcoder" \
        --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": "pavankumarbalijepalli/phi2-sqlcoder",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use pavankumarbalijepalli/phi2-sqlcoder with Ollama:
```
ollama run hf.co/pavankumarbalijepalli/phi2-sqlcoder:F16
```

Unsloth Studio new

How to use pavankumarbalijepalli/phi2-sqlcoder with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for pavankumarbalijepalli/phi2-sqlcoder to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for pavankumarbalijepalli/phi2-sqlcoder to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for pavankumarbalijepalli/phi2-sqlcoder to start chatting

Docker Model Runner
How to use pavankumarbalijepalli/phi2-sqlcoder with Docker Model Runner:
```
docker model run hf.co/pavankumarbalijepalli/phi2-sqlcoder:F16
```

Lemonade

How to use pavankumarbalijepalli/phi2-sqlcoder with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pavankumarbalijepalli/phi2-sqlcoder:F16

Run and chat with the model

lemonade run user.phi2-sqlcoder-F16

List all available models

lemonade list

pavankumarbalijepalli commited on Mar 14, 2024

Commit

7a5dc3a

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Update README.md

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@@ -38,14 +38,14 @@ This model has been finetuned with `b-mc2/sql-create-context` on `microsoft/phi-
 - **Model type:** CASUAL_LM
 - **Language(s) (NLP):** English, SQL
 - **License:** MIT
-- **Finetuned from model [optional]:** [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)
-### Model Sources [optional]
 <!-- Provide the basic links for the model. -->
 - **Repository:** [pavankumarbalijepalli/pr-phi2-vs-defog](https://github.com/pavankumarbalijepalli/pr-phi2-vs-defog/)
-- **Paper [optional]:** [BITS Project Paper](https://github.com/pavankumarbalijepalli/pr-phi2-vs-defog/blob/main/2021SC04115%20-%20Final.pdf)
 ## Uses
@@ -156,7 +156,6 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
 ```
 @misc{b-mc2_2023_sql-create-context,
   title   = {sql-create-context Dataset},
@@ -235,7 +234,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 - **Carbon Emitted:** 2.52 kg eq. CO2
-## Citation [optional]
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

 - **Model type:** CASUAL_LM
 - **Language(s) (NLP):** English, SQL
 - **License:** MIT
+- **Finetuned from model:** [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)
+### Model Sources
 <!-- Provide the basic links for the model. -->
 - **Repository:** [pavankumarbalijepalli/pr-phi2-vs-defog](https://github.com/pavankumarbalijepalli/pr-phi2-vs-defog/)
+- **Paper :** [BITS Project Paper](https://github.com/pavankumarbalijepalli/pr-phi2-vs-defog/blob/main/2021SC04115%20-%20Final.pdf)
 ## Uses
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 ```
 @misc{b-mc2_2023_sql-create-context,
   title   = {sql-create-context Dataset},
 - **Carbon Emitted:** 2.52 kg eq. CO2
+## Citation
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->