Instructions to use alwint3r/TinyLlama-1.1B-Chat-v1.0-sql-create-context-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alwint3r/TinyLlama-1.1B-Chat-v1.0-sql-create-context-mlx with Transformers:

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

pipe = pipeline("text-generation", model="alwint3r/TinyLlama-1.1B-Chat-v1.0-sql-create-context-mlx")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alwint3r/TinyLlama-1.1B-Chat-v1.0-sql-create-context-mlx")
model = AutoModelForCausalLM.from_pretrained("alwint3r/TinyLlama-1.1B-Chat-v1.0-sql-create-context-mlx", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use alwint3r/TinyLlama-1.1B-Chat-v1.0-sql-create-context-mlx with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alwint3r/TinyLlama-1.1B-Chat-v1.0-sql-create-context-mlx"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alwint3r/TinyLlama-1.1B-Chat-v1.0-sql-create-context-mlx",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/alwint3r/TinyLlama-1.1B-Chat-v1.0-sql-create-context-mlx

SGLang

How to use alwint3r/TinyLlama-1.1B-Chat-v1.0-sql-create-context-mlx 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 "alwint3r/TinyLlama-1.1B-Chat-v1.0-sql-create-context-mlx" \
    --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": "alwint3r/TinyLlama-1.1B-Chat-v1.0-sql-create-context-mlx",
		"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 "alwint3r/TinyLlama-1.1B-Chat-v1.0-sql-create-context-mlx" \
        --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": "alwint3r/TinyLlama-1.1B-Chat-v1.0-sql-create-context-mlx",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use alwint3r/TinyLlama-1.1B-Chat-v1.0-sql-create-context-mlx with Docker Model Runner:
```
docker model run hf.co/alwint3r/TinyLlama-1.1B-Chat-v1.0-sql-create-context-mlx
```

TinyLlama-1.1B-Chat-v1.0 Fine-tuned on sql-create-context Data

This model was fine-tuned to generate SQL queries from natural language questions given the context of a table DDL.

See original models here.

See the sql-create-context dataset here.

Usage

Given the following prompt:

<|system|>
You are a chatbot who can help code and translate natural language to SQL queries.</s>
<|user|>
SQL Table Context:
CREATE TABLE table_name_2 (wins INTEGER, losses VARCHAR, ties VARCHAR, goals_against VARCHAR)

What is the total wins with less than 2 ties, 18 goals, and less than 2 losses?</s>
<|assistant|>

Downloads last month: 13

Safetensors

Model size

1B params

Tensor type

F16

alwint3r
/

TinyLlama-1.1B-Chat-v1.0-sql-create-context-mlx

TinyLlama-1.1B-Chat-v1.0 Fine-tuned on sql-create-context Data

Usage

Dataset used to train alwint3r/TinyLlama-1.1B-Chat-v1.0-sql-create-context-mlx