nvidia/HelpSteer3
Viewer • Updated • 133k • 6.98k • 109
How to use soundTeam/Q3-8B-Kintsugi_mlx-8bpw with MLX:
# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm
# Generate text with mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("soundTeam/Q3-8B-Kintsugi_mlx-8bpw")
prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
text = generate(model, tokenizer, prompt=prompt, verbose=True)How to use soundTeam/Q3-8B-Kintsugi_mlx-8bpw with Unsloth Studio:
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for soundTeam/Q3-8B-Kintsugi_mlx-8bpw to start chatting
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for soundTeam/Q3-8B-Kintsugi_mlx-8bpw to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for soundTeam/Q3-8B-Kintsugi_mlx-8bpw to start chatting
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="soundTeam/Q3-8B-Kintsugi_mlx-8bpw",
max_seq_length=2048,
)How to use soundTeam/Q3-8B-Kintsugi_mlx-8bpw with MLX LM:
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "soundTeam/Q3-8B-Kintsugi_mlx-8bpw"
# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "soundTeam/Q3-8B-Kintsugi_mlx-8bpw"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "soundTeam/Q3-8B-Kintsugi_mlx-8bpw",
"messages": [
{"role": "user", "content": "Hello"}
]
}'This model soundTeam/Q3-8B-Kintsugi_mlx-8bpw was converted to MLX format from allura-org/Q3-8B-Kintsugi using mlx-lm version 0.25.2.
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("soundTeam/Q3-8B-Kintsugi_mlx-8bpw")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
8-bit