Update README.md

README.md

@@ -8,11 +8,27 @@ tags:
   - nlp
   - code
 ---
+# 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:
@@ -23,7 +39,25 @@ MobiLlama-05B has been integrated in the development version (4.37.0.dev) of `tr
 
 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())
+
+```
+
 ## Intended Uses
 
-Given the nature of the training data, the Phi-2 model is best suited for prompts using the QA format, the chat format, and the code format.
+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.