Model: mlx-community/Nemotron-Mini-4B-Instruct-bf16-mlx Source: Original Platform
language, license, license_name, license_link, tags, base_model
| language | license | license_name | license_link | tags | base_model | |||||
|---|---|---|---|---|---|---|---|---|---|---|
|
other | nvidia-open-model-license | https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf |
|
nvidia/Nemotron-Mini-4B-Instruct |
mlx-community/Nemotron-Mini-4B-Instruct-bf16-mlx
This model was converted from nvidia/Nemotron-Mini-4B-Instruct to MLX format for use on Apple Silicon.
Quantization: No quantization – full bfloat16
Usage
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/{repo_name}")
prompt = (
"<extra_id_0>System\\n"
"You are a helpful, honest AI assistant.\\n\\n"
"<extra_id_1>User\\n"
"Who are you?\\n"
"<extra_id_1>Assistant\\n"
)
print(generate(model, tokenizer, prompt, max_tokens=256))
Benchmark (Apple Silicon, single prompt, 23 tokens)
| Variant | tok/s |
|---|---|
| bf16 (this) | 2.47 |
| 4-bit default | 4.37 |
| mxfp4-q4 | 4.56 |
| nvfp4-q4 | 9.69 |
| mixed-3-6 | 9.72 |
Original model
See nvidia/Nemotron-Mini-4B-Instruct for the original model card, license, and usage terms.
Description
Languages
Jinja
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