Instructions to use ekshat/Llama-2-7b-chat-finetune-for-text2sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ekshat/Llama-2-7b-chat-finetune-for-text2sql with Transformers:

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

pipe = pipeline("text-generation", model="ekshat/Llama-2-7b-chat-finetune-for-text2sql")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ekshat/Llama-2-7b-chat-finetune-for-text2sql")
model = AutoModelForCausalLM.from_pretrained("ekshat/Llama-2-7b-chat-finetune-for-text2sql", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ekshat/Llama-2-7b-chat-finetune-for-text2sql with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ekshat/Llama-2-7b-chat-finetune-for-text2sql

SGLang

How to use ekshat/Llama-2-7b-chat-finetune-for-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 "ekshat/Llama-2-7b-chat-finetune-for-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": "ekshat/Llama-2-7b-chat-finetune-for-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 "ekshat/Llama-2-7b-chat-finetune-for-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": "ekshat/Llama-2-7b-chat-finetune-for-text2sql",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ekshat/Llama-2-7b-chat-finetune-for-text2sql with Docker Model Runner:
```
docker model run hf.co/ekshat/Llama-2-7b-chat-finetune-for-text2sql
```

ekshat commited on Sep 12, 2023

Commit

cfcf518

1 Parent(s): 4556c33

Update README.md

Browse files

Files changed (1) hide show

README.md +8 -8

README.md CHANGED Viewed

@@ -25,7 +25,7 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 tokenizer = AutoTokenizer.from_pretrained("ekshat/Llama-2-7b-chat-finetune-for-text2sql")
 model = AutoModelForCausalLM.from_pretrained("ekshat/Llama-2-7b-chat-finetune-for-text2sql")
-# Run text generation pipeline with our next model
 context = "CREATE TABLE Student (name VARCHAR, college VARCHAR, age VARCHAR, group VARCHAR, marks VARCHAR)"
 question = "List the name of Students belongs to school 'St. Xavier' and having marks greater than '600'"
@@ -37,14 +37,14 @@ prompt = f"""Below is an context that describes a sql query, paired with an ques
 ### Answer:"""
 sequences = pipeline(
-prompt,
-do_sample=True,
-top_k=10,
-num_return_sequences=1,
-eos_token_id=tokenizer.eos_token_id,
-max_length=200,
 )
 for seq in sequences:
-print(f"Result: {seq['generated_text']}")
 ```

 tokenizer = AutoTokenizer.from_pretrained("ekshat/Llama-2-7b-chat-finetune-for-text2sql")
 model = AutoModelForCausalLM.from_pretrained("ekshat/Llama-2-7b-chat-finetune-for-text2sql")
+# Run text generation pipeline with our model
 context = "CREATE TABLE Student (name VARCHAR, college VARCHAR, age VARCHAR, group VARCHAR, marks VARCHAR)"
 question = "List the name of Students belongs to school 'St. Xavier' and having marks greater than '600'"
 ### Answer:"""
 sequences = pipeline(
+                      prompt,
+                      do_sample=True,
+                      top_k=10,
+                      num_return_sequences=1,
+                      eos_token_id=tokenizer.eos_token_id,
+                      max_length=200,
 )
 for seq in sequences:
+  print(f"Result: {seq['generated_text']}")
 ```