Instructions to use maxrubin629/Nemotron-H-8B-Reasoning-128K-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use maxrubin629/Nemotron-H-8B-Reasoning-128K-6bit 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("maxrubin629/Nemotron-H-8B-Reasoning-128K-6bit") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use maxrubin629/Nemotron-H-8B-Reasoning-128K-6bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "maxrubin629/Nemotron-H-8B-Reasoning-128K-6bit" --prompt "Once upon a time"
maxrubin629/Nemotron-H-8B-Reasoning-128K-6bit
This model maxrubin629/Nemotron-H-8B-Reasoning-128K-6bit was converted to MLX format from nvidia/Nemotron-H-8B-Reasoning-128K using mlx-lm version 0.27.1.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("maxrubin629/Nemotron-H-8B-Reasoning-128K-6bit")
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)
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Model size
8B params
Tensor type
BF16
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Hardware compatibility
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Model tree for maxrubin629/Nemotron-H-8B-Reasoning-128K-6bit
Base model
nvidia/Nemotron-H-8B-Reasoning-128K