Instructions to use support-pvelocity/Code-Llama-2-7B-instruct-text2sql-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use support-pvelocity/Code-Llama-2-7B-instruct-text2sql-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="support-pvelocity/Code-Llama-2-7B-instruct-text2sql-GGUF",
	filename="Code-Llama-2-7B-instruct-text2sql.q4_k_m.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use support-pvelocity/Code-Llama-2-7B-instruct-text2sql-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf support-pvelocity/Code-Llama-2-7B-instruct-text2sql-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf support-pvelocity/Code-Llama-2-7B-instruct-text2sql-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf support-pvelocity/Code-Llama-2-7B-instruct-text2sql-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf support-pvelocity/Code-Llama-2-7B-instruct-text2sql-GGUF:Q4_K_M

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 support-pvelocity/Code-Llama-2-7B-instruct-text2sql-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf support-pvelocity/Code-Llama-2-7B-instruct-text2sql-GGUF:Q4_K_M

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 support-pvelocity/Code-Llama-2-7B-instruct-text2sql-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf support-pvelocity/Code-Llama-2-7B-instruct-text2sql-GGUF:Q4_K_M

Use Docker

docker model run hf.co/support-pvelocity/Code-Llama-2-7B-instruct-text2sql-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use support-pvelocity/Code-Llama-2-7B-instruct-text2sql-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "support-pvelocity/Code-Llama-2-7B-instruct-text2sql-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "support-pvelocity/Code-Llama-2-7B-instruct-text2sql-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/support-pvelocity/Code-Llama-2-7B-instruct-text2sql-GGUF:Q4_K_M

Ollama
How to use support-pvelocity/Code-Llama-2-7B-instruct-text2sql-GGUF with Ollama:
```
ollama run hf.co/support-pvelocity/Code-Llama-2-7B-instruct-text2sql-GGUF:Q4_K_M
```

Unsloth Studio new

How to use support-pvelocity/Code-Llama-2-7B-instruct-text2sql-GGUF 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 support-pvelocity/Code-Llama-2-7B-instruct-text2sql-GGUF 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 support-pvelocity/Code-Llama-2-7B-instruct-text2sql-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for support-pvelocity/Code-Llama-2-7B-instruct-text2sql-GGUF to start chatting

Docker Model Runner
How to use support-pvelocity/Code-Llama-2-7B-instruct-text2sql-GGUF with Docker Model Runner:
```
docker model run hf.co/support-pvelocity/Code-Llama-2-7B-instruct-text2sql-GGUF:Q4_K_M
```

Lemonade

How to use support-pvelocity/Code-Llama-2-7B-instruct-text2sql-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull support-pvelocity/Code-Llama-2-7B-instruct-text2sql-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Code-Llama-2-7B-instruct-text2sql-GGUF-Q4_K_M

List all available models

lemonade list

support-pvelocity commited on Nov 7, 2023

Commit

dcfa676

1 Parent(s): 4ede23e

Update README.md

Browse files

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README.md +2 -1

README.md CHANGED Viewed

@@ -58,7 +58,8 @@ model = AutoModelForCausalLM.from_pretrained(
     model_name,
     model_file=model_name.split('/')[1].replace('-GGUF', '.q4_k_m.gguf'),
     model_type="llama",
-    gpu_layers=50
 )
 table = "CREATE TABLE sales ( sale_id number PRIMARY KEY, product_id number, customer_id number, salesperson_id number, sale_date DATE, quantity number, FOREIGN KEY (product_id) REFERENCES products(product_id), FOREIGN KEY (customer_id) REFERENCES customers(customer_id), FOREIGN KEY (salesperson_id) REFERENCES salespeople(salesperson_id)); CREATE TABLE product_suppliers ( supplier_id number PRIMARY KEY, product_id number, supply_price number, FOREIGN KEY (product_id) REFERENCES products(product_id)); CREATE TABLE customers ( customer_id number PRIMARY KEY, name text, address text ); CREATE TABLE salespeople ( salesperson_id number PRIMARY KEY, name text, region text ); CREATE TABLE product_suppliers ( supplier_id number PRIMARY KEY, product_id number, supply_price number );"

     model_name,
     model_file=model_name.split('/')[1].replace('-GGUF', '.q4_k_m.gguf'),
     model_type="llama",
+    gpu_layers=50,
+    context_length=4048
 )
 table = "CREATE TABLE sales ( sale_id number PRIMARY KEY, product_id number, customer_id number, salesperson_id number, sale_date DATE, quantity number, FOREIGN KEY (product_id) REFERENCES products(product_id), FOREIGN KEY (customer_id) REFERENCES customers(customer_id), FOREIGN KEY (salesperson_id) REFERENCES salespeople(salesperson_id)); CREATE TABLE product_suppliers ( supplier_id number PRIMARY KEY, product_id number, supply_price number, FOREIGN KEY (product_id) REFERENCES products(product_id)); CREATE TABLE customers ( customer_id number PRIMARY KEY, name text, address text ); CREATE TABLE salespeople ( salesperson_id number PRIMARY KEY, name text, region text ); CREATE TABLE product_suppliers ( supplier_id number PRIMARY KEY, product_id number, supply_price number );"