Instructions to use alwint3r/TinyLlama-1.1B-Chat-v0.1-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-v0.1-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-v0.1-sql-create-context-mlx")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alwint3r/TinyLlama-1.1B-Chat-v0.1-sql-create-context-mlx")
model = AutoModelForCausalLM.from_pretrained("alwint3r/TinyLlama-1.1B-Chat-v0.1-sql-create-context-mlx")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use alwint3r/TinyLlama-1.1B-Chat-v0.1-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-v0.1-sql-create-context-mlx"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alwint3r/TinyLlama-1.1B-Chat-v0.1-sql-create-context-mlx",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/alwint3r/TinyLlama-1.1B-Chat-v0.1-sql-create-context-mlx

SGLang

How to use alwint3r/TinyLlama-1.1B-Chat-v0.1-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-v0.1-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/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alwint3r/TinyLlama-1.1B-Chat-v0.1-sql-create-context-mlx",
		"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 "alwint3r/TinyLlama-1.1B-Chat-v0.1-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/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alwint3r/TinyLlama-1.1B-Chat-v0.1-sql-create-context-mlx",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use alwint3r/TinyLlama-1.1B-Chat-v0.1-sql-create-context-mlx with Docker Model Runner:
```
docker model run hf.co/alwint3r/TinyLlama-1.1B-Chat-v0.1-sql-create-context-mlx
```

TinyLlama-1.1B-Chat-v0.1 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

Example prompt:

### SQL Table Context: CREATE TABLE table_2249029_1 (gdp_world_rank VARCHAR, asian_rank VARCHAR)
### Human: Name the gdp world rank for asian rank being 15

Example response:

### Assistant: SELECT gdp_world_rank FROM table_2249029_1 WHERE asian_rank = "15"

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alwint3r
/

TinyLlama-1.1B-Chat-v0.1-sql-create-context-mlx

TinyLlama-1.1B-Chat-v0.1 Fine-tuned on sql-create-context Data

Usage

Dataset used to train alwint3r/TinyLlama-1.1B-Chat-v0.1-sql-create-context-mlx