Instructions to use luiscalisto/SalamandraTA-7B-Instruct-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use luiscalisto/SalamandraTA-7B-Instruct-MLX-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir SalamandraTA-7B-Instruct-MLX-4bit luiscalisto/SalamandraTA-7B-Instruct-MLX-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
SalamandraTA-7B-Instruct-MLX-4bit
MLX 4-bit quantisation of BSC-LT/salamandraTA-7b-instruct,
converted for use on Apple Silicon via mlx-lm.
Source model
- Repository:
BSC-LT/salamandraTA-7b-instruct - Release: 2024-12
- Family: salamandra
- Origin: eu
- Languages / coverage: 35 EU languages + 3 varieties, translation-specialised
- License: apache-2.0 (inherited)
Notes from upstream
Translation-focused fine-tune of SalamandraTA-7b-base. Broad EU coverage (35 languages + 3 varieties). Apache 2.0.
Conversion details
- Tool:
mlx-lm0.31.3 - Quantisation: 4-bit (defaults from
mlx_lm.convert) - Converted on: 2026-05-05
Usage
from mlx_lm import load, generate
model, tokenizer = load("luiscalisto/SalamandraTA-7B-Instruct-MLX-4bit")
prompt = "Hello, who are you?"
print(generate(model, tokenizer, prompt=prompt, max_tokens=128, verbose=False))
License and attribution
This is a quantised redistribution of BSC-LT/salamandraTA-7b-instruct. The original model and
its license terms (apache-2.0) carry through unchanged. Please cite the
upstream authors when using this model. See the source repository for the
authoritative model card and citation.
Conversion provenance
Produced by llm-mlx-conversions,
a small utility for publishing community MLX 4-bit quants of open-weight LLMs.
- Downloads last month
- 5
Model size
1B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
Log In to add your hardware
4-bit
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support