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---
license: mit
license_link: https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE
language:
- en
pipeline_tag: text-generation
tags:
- nlp
- code
datasets:
- LLM360/AmberDatasets
---
# MobiLlama-05B
<center><img src="MobileLLaMa.png" alt="mobillama logo" width="300"/></center>
## Model Summary
MobiLlama-05B is a Small Language Model with **0.5 billion** parameters. It was trained using the Amber data sources [Amber-Dataset](https://huggingface.co/datasets/LLM360/AmberDatasets).
## Model Description
- **Model type:** Small Language Model (SLM) built using the architecture design of LLaMA-7B
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Resources for more information:**
- [Training Code](https://github.com/LLM360/amber-train)
- [Data Preparation](https://github.com/LLM360/amber-data-prep)
- [Metrics](https://github.com/LLM360/Analysis360)
- [Fully processed Amber pretraining data](https://huggingface.co/datasets/LLM360/AmberDatasets)
## How to Use
MobiLlama-05B has been integrated in the development version (4.37.0.dev) of `transformers`. Until the official version is released through `pip`, ensure that you are doing one of the following:
* When loading the model, ensure that `trust_remote_code=True` is passed as an argument of the `from_pretrained()` function.
* Update your local `transformers` to the development version: `pip uninstall -y transformers && pip install git+https://github.com/huggingface/transformers`. The previous command is an alternative to cloning and installing from the source.
The current `transformers` version can be verified with: `pip list | grep transformers`.
To load a specific checkpoint, simply pass a revision with a value between `"ckpt_000"` and `"ckpt_358"`. If no revision is provided, it will load `"ckpt_359"`, which is the final checkpoint.
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
torch.set_default_device("cuda")
model = AutoModelForCausalLM.from_pretrained("MBZUAI/MobiLlama-05B", torch_dtype="auto", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("MBZUAI/MobiLlama-05B", trust_remote_code=True)
text = "Write a C language program to find fibonnaci series?"
input_ids = tokenizer(text, return_tensors="pt").to('cuda').input_ids
outputs = model.generate(input_ids, max_length=1000, repetition_penalty=1.2, pad_token_id=tokenizer.eos_token_id)
print(tokenizer.batch_decode(outputs[:, input_ids.shape[1]:-1])[0].strip())
```
## Evaluation
| Evaluation Benchmark | MobiLlama-0.5B | MobiLlama-0.8B | MobiLlama-1.2B |
| ----------- | ----------- | ----------- |
| HellaSwag | 0.5252 | 0.5409 | 0.6299 |
| MMLU | 0.2645 | 0.2692 | 0.2423 |
| Arc Challenge | 0.2952 | 0.3020 | 0.3455 |
| TruthfulQA | 0.3805 | 0.3848 | 0.3557 |
| CrowsPairs | 0.6403 | 0.6482 | 0.6812 |
| PIQA | 0.7203 | 0.7317 | 0.7529 |
| Race | 0.3368 | 0.3337 | 0.3531 |
| SIQA | 0.4022 | 0.4160 | 0.4196 |
| Winogrande | 0.5753 | 0.5745 | 0.6108 |
## Intended Uses
Given the nature of the training data, the MobiLlama-05B model is best suited for prompts using the QA format, the chat format, and the code format. |