Instructions to use mlx-community/sarvam-translate-mlx-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlx-community/sarvam-translate-mlx-bf16 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="mlx-community/sarvam-translate-mlx-bf16")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("mlx-community/sarvam-translate-mlx-bf16") model = AutoModelForImageTextToText.from_pretrained("mlx-community/sarvam-translate-mlx-bf16") - MLX
How to use mlx-community/sarvam-translate-mlx-bf16 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir sarvam-translate-mlx-bf16 mlx-community/sarvam-translate-mlx-bf16
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
mlx-community/sarvam-translate-mlx-bf16
The Model mlx-community/sarvam-translate-mlx-bf16 was converted to MLX format from sarvamai/sarvam-translate using mlx-lm version 0.22.3.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("bibproj/sarvam-translate-mlx-fp16")
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
- 44
Hardware compatibility
Log In to add your hardware
Quantized