Instructions to use artificialguybr/llama3-8b-sql-create-context with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use artificialguybr/llama3-8b-sql-create-context with Transformers:

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

pipe = pipeline("text-generation", model="artificialguybr/llama3-8b-sql-create-context")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("artificialguybr/llama3-8b-sql-create-context")
model = AutoModelForCausalLM.from_pretrained("artificialguybr/llama3-8b-sql-create-context")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use artificialguybr/llama3-8b-sql-create-context with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "artificialguybr/llama3-8b-sql-create-context"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "artificialguybr/llama3-8b-sql-create-context",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/artificialguybr/llama3-8b-sql-create-context

SGLang

How to use artificialguybr/llama3-8b-sql-create-context 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 "artificialguybr/llama3-8b-sql-create-context" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "artificialguybr/llama3-8b-sql-create-context",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "artificialguybr/llama3-8b-sql-create-context" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "artificialguybr/llama3-8b-sql-create-context",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use artificialguybr/llama3-8b-sql-create-context with Docker Model Runner:
```
docker model run hf.co/artificialguybr/llama3-8b-sql-create-context
```

artificialguybr commited on Apr 24, 2024

Commit

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@@ -80,22 +80,32 @@ special_tokens:
 # out-llama8b-createcontext
-This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B) on the None dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.0201
 ## Model description
-More information needed
-## Intended uses & limitations
-More information needed
-## Training and evaluation data
-More information needed
 ## Training procedure
 ### Training hyperparameters

 # out-llama8b-createcontext
+This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B) on the [b-mc2/sql-create-context](https://huggingface.co/datasets/b-mc2/sql-create-context) dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.0201
 ## Model description
+The model is a text-to-SQL language model designed to generate SQL queries from natural language inputs. It takes as input a natural language question and a SQL CREATE TABLE statement as context, and outputs a SQL query that answers the question based on the provided table schema.
+The model is trained on a dataset of 78,577 examples, which combines the WikiSQL and Spider datasets. The dataset is specifically designed to prevent hallucination of column and table names, a common issue in text-to-SQL models. The CREATE TABLE statement provides the necessary context for the model to generate accurate SQL queries without requiring actual rows of data.
+The model is intended to be used in applications where the table schema is known, and the goal is to generate SQL queries that answer specific questions based on that schema. The model can be fine-tuned for specific use cases and SQL dialects.
+## Intended uses & limitations
+Intended uses:
+Generating SQL queries from natural language inputs in applications where the table schema is known
+Supporting data analysis and visualization tasks in various domains
+Integrating with other language models or tools to provide a more comprehensive data analysis pipeline
+Limitations:
+The model relies on the accuracy of the provided CREATE TABLE statement and may not perform well if the schema is incomplete or incorrect
+The model may not generalize well to unseen SQL dialects or table schemas
+The model may not be able to handle complex queries that require multiple joins or subqueries
+The model may not be able to handle queries that require external knowledge or common sense
+The model may not be able to handle queries that are ambiguous or open-ended
 ## Training procedure
 ### Training hyperparameters