Instructions to use meetween/Llama-speechlmm-1.0-l-MT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use meetween/Llama-speechlmm-1.0-l-MT 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="meetween/Llama-speechlmm-1.0-l-MT")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("meetween/Llama-speechlmm-1.0-l-MT", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Model Information
This is the version of meetween/Llama-speechlmm-1.0-l that was fine-tuned for Speech-to-Text Translation.
License: see LICENSE
Model Architecture
Identical to the base model. The model was obtained by training LoRA on the LLM. This repository contains the model weights with LoRA merged into the main weights.
How to Use
Identical to the base model.
Fine-tuning Data
This model has been fine-tuned on the same EuroParl-ST machine translation data ({en, fr, it, de, es} → {en, fr, it, de, es}) from the training data of the base model.
Evaluation Results
|
DATASET: |
FLORES |
ACL 60/60 |
AVG |
||||
|
BLEU |
en-de |
en-es |
en-it |
en-fr |
en-fr |
en-de |
|
|
Llama3-instruct (D5) |
28.1 |
24.4 |
25.0 |
41.2 |
48.8 |
34.2 |
33.6 |
|
NLLB (D5) |
39.4 |
23.7 |
31.2 |
50.7 |
59.1 |
45.2 |
41.6 |
|
SpeechLMM_v1.0_L |
29.4 |
22.3 |
20.1 |
31.9 |
35.5 |
32.8 |
28.7 |
|
Speech LMM v1.0_L-FT (LoRA) |
20.0 |
16.0 |
11.6 |
21.8 |
24.9 |
20.7 |
19.2 |
Framework Versions
- Transformers 4.45.0
- Pytorch 2.3.1+cu124.post2
- Datasets 3.2.0
- Tokenizers 0.20.0
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Model tree for meetween/Llama-speechlmm-1.0-l-MT
Base model
meetween/Llama-speechlmm-1.0-l