Instructions to use Sharpaxis/Llama-2-7b-chat-finetune_TEXT2SQL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sharpaxis/Llama-2-7b-chat-finetune_TEXT2SQL with Transformers:

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

pipe = pipeline("text-generation", model="Sharpaxis/Llama-2-7b-chat-finetune_TEXT2SQL")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Sharpaxis/Llama-2-7b-chat-finetune_TEXT2SQL")
model = AutoModelForCausalLM.from_pretrained("Sharpaxis/Llama-2-7b-chat-finetune_TEXT2SQL")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Sharpaxis/Llama-2-7b-chat-finetune_TEXT2SQL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sharpaxis/Llama-2-7b-chat-finetune_TEXT2SQL"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sharpaxis/Llama-2-7b-chat-finetune_TEXT2SQL",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Sharpaxis/Llama-2-7b-chat-finetune_TEXT2SQL

SGLang

How to use Sharpaxis/Llama-2-7b-chat-finetune_TEXT2SQL 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 "Sharpaxis/Llama-2-7b-chat-finetune_TEXT2SQL" \
    --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": "Sharpaxis/Llama-2-7b-chat-finetune_TEXT2SQL",
		"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 "Sharpaxis/Llama-2-7b-chat-finetune_TEXT2SQL" \
        --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": "Sharpaxis/Llama-2-7b-chat-finetune_TEXT2SQL",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Sharpaxis/Llama-2-7b-chat-finetune_TEXT2SQL with Docker Model Runner:
```
docker model run hf.co/Sharpaxis/Llama-2-7b-chat-finetune_TEXT2SQL
```

datasets:

ekshat/text-2-sql-with-context language:
en library_name: transformers pipeline_tag: text-generation tags:
text-2-sql
text-generation
text2sql

Inference

!pip install transformers accelerate xformers bitsandbytes

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

tokenizer = AutoTokenizer.from_pretrained("ekshat/Llama-2-7b-chat-finetune-for-text2sql")

# Loading model in 4 bit precision
model = AutoModelForCausalLM.from_pretrained("ekshat/Llama-2-7b-chat-finetune-for-text2sql", load_in_4bit=True)

context = "CREATE TABLE head (name VARCHAR, born_state VARCHAR, age VARCHAR)"
question = "List the name, born state and age of the heads of departments ordered by age."

prompt = f"""Below is an context that describes a sql query, paired with an question that provides further information. Write an answer that appropriately completes the request.
### Context:
{context}
### Question:
{question}
### Answer:"""

pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
result = pipe(prompt)
print(result[0]['generated_text'])

Model Information

model_name = "NousResearch/Llama-2-7b-chat-hf"
dataset_name = "ekshat/text-2-sql-with-context"

QLoRA parameters

lora_r = 64
lora_alpha = 16
lora_dropout = 0.1

BitsAndBytes parameters

use_4bit = True
bnb_4bit_compute_dtype = "float16"
bnb_4bit_quant_type = "nf4"
use_nested_quant = False

Training Arguments parameters

num_train_epochs = 1
fp16 = False
bf16 = False
per_device_train_batch_size = 8
per_device_eval_batch_size = 4
gradient_accumulation_steps = 1
gradient_checkpointing = True
max_grad_norm = 0.3
learning_rate = 2e-4
weight_decay = 0.001
optim = "paged_adamw_32bit"
lr_scheduler_type = "cosine"
max_steps = -1
warmup_ratio = 0.03
group_by_length = True
save_steps = 0
logging_steps = 25

SFT parameters

max_seq_length = None
packing = False

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