license: apache-2.0
pipeline_tag: text-generation
language:
- ta
tags:
- pretrained
inference:
parameters:
temperature: 0.7
datasets:
- Hemanth-thunder/tamil-madlad-400
Model Card for Tamil-Mistral-7B-v0.1
The Tamil-Mistral-7B-v0.1 Large Language Model (LLM) is a pre-trained generative text model trained at the top of mistral base model 7 billion parameters. This is extends version of tokenization capability by increasing tamil tokens by 20k. Additionally, it was Pretrained on 1.19 million Tamil documents sourced from madlad-400 (Tamil) MADLAD-400 (Multilingual Audited Dataset: Low-resource And Document-level).
pretraining time: 145 hours (GPU NVIDIA RTX A6000 48GB)
Mistral model details
For full details of this model please read our paper and release blog post.
Model Architecture
Mistral-7B-v0.1 is a transformer model, with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
Running the model on a GPU
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Hemanth-thunder/Tamil-Mistral-7B-v0.1")
model = AutoModelForCausalLM.from_pretrained("Hemanth-thunder/Tamil-Mistral-7B-v0.1")
input_text = "ஒரு கிராமத்தில் பண்ணையார் ஒருவர் வாழ்ந்து வந்தார்."
input_ids = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
Troubleshooting
- If you see the following error:
KeyError: 'mistral'
- Or:
NotImplementedError: Cannot copy out of meta tensor; no data!
Ensure you are utilizing a stable version of Transformers, 4.34.0 or newer.
Notice
Mistral 7B is a pretrained base model and therefore does not have any moderation mechanisms.
The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.