Instructions to use QuantFactory/llama-3-sqlcoder-8b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/llama-3-sqlcoder-8b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/llama-3-sqlcoder-8b-GGUF",
	filename="llama-3-sqlcoder-8b.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/llama-3-sqlcoder-8b-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/llama-3-sqlcoder-8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/llama-3-sqlcoder-8b-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 QuantFactory/llama-3-sqlcoder-8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/llama-3-sqlcoder-8b-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 QuantFactory/llama-3-sqlcoder-8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/llama-3-sqlcoder-8b-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 QuantFactory/llama-3-sqlcoder-8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/llama-3-sqlcoder-8b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/llama-3-sqlcoder-8b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/llama-3-sqlcoder-8b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/llama-3-sqlcoder-8b-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/llama-3-sqlcoder-8b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/llama-3-sqlcoder-8b-GGUF:Q4_K_M

Ollama
How to use QuantFactory/llama-3-sqlcoder-8b-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/llama-3-sqlcoder-8b-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/llama-3-sqlcoder-8b-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 QuantFactory/llama-3-sqlcoder-8b-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 QuantFactory/llama-3-sqlcoder-8b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/llama-3-sqlcoder-8b-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/llama-3-sqlcoder-8b-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/llama-3-sqlcoder-8b-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/llama-3-sqlcoder-8b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/llama-3-sqlcoder-8b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.llama-3-sqlcoder-8b-GGUF-Q4_K_M

List all available models

lemonade list

munish0838 commited on May 29, 2024

Commit

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+---
+license: cc-by-sa-4.0
+metrics:
+- accuracy
+pipeline_tag: text-generation
+tags:
+- code
+base_model: defog/llama-3-sqlcoder-8b
+---
+# QuantFactory/llama-3-sqlcoder-8b-GGUF
+This is quantized version of [defog/llama-3-sqlcoder-8b](https://huggingface.co/defog/llama-3-sqlcoder-8b) created using llama.cpp
+## Model Description
+A capable language model for text to SQL generation for Postgres, Redshift and Snowflake that is on-par with the most capable generalist frontier models.
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/603bbad3fd770a9997b57cb6/h52Z_OKYBaDDQMFZyU5pF.png)
+Developed by: Defog, Inc
+Model type: [Text to SQL]
+License: [CC-by-SA-4.0]
+Finetuned from model: [Meta-Llama-3-8B-Instruct]
+## Demo Page
+[https://defog.ai/sqlcoder-demo/](https://defog.ai/sqlcoder-demo/)
+## Ideal prompt and inference parameters
+Set temperature to 0, and do not do sampling.
+### Prompt
+```
+<|begin_of_text|><|start_header_id|>user<|end_header_id|>
+Generate a SQL query to answer this question: `{user_question}`
+{instructions}
+DDL statements:
+{create_table_statements}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+The following SQL query best answers the question `{user_question}`:
+```sql
+```
+## Evaluation
+This model was evaluated on SQL-Eval, a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities.
+You can read more about the methodology behind SQLEval [here](https://defog.ai/blog/open-sourcing-sqleval/).
+## Contact defog
+Contact defog on X at [@defogdata](https://twitter.com/defogdata), or on email at founders@defog.ai