Instructions to use rizalhilman/wikisql-4bit-1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use rizalhilman/wikisql-4bit-1k with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("rizalhilman/wikisql-4bit-1k") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- MLX LM
How to use rizalhilman/wikisql-4bit-1k with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "rizalhilman/wikisql-4bit-1k" --prompt "Once upon a time"
rizalhilman/wikisql-4bit-1k
The Model rizalhilman/wikisql-4bit-1k was converted to MLX format from mistralai/Mistral-7B-v0.1 using mlx-lm version 0.19.3.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("rizalhilman/wikisql-4bit-1k")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
- Downloads last month
- 2
Model size
1B params
Tensor type
F16
·
U32 ·
Hardware compatibility
Log In to add your hardware
4-bit
Model tree for rizalhilman/wikisql-4bit-1k
Base model
mistralai/Mistral-7B-v0.1