Enhance model card with metadata, paper link, and basic usage (#1)

- Enhance model card with metadata, paper link, and basic usage (400109fd74b565af86dd83d6be47b58b7051daf4)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,17 +1,45 @@
 ---
-datasets:
-- BAAI/Infinity-Instruct
+license: mit
+library_name: transformers
+pipeline_tag: text-generation
 base_model:
 - nvidia/Llama-3.1-Minitron-4B-Depth-Base
+datasets:
+- BAAI/Infinity-Instruct
 ---
 
-We fine-tune nvidia/Llama-3.1-Minitron-4B-Depth-Base with LLM-Neo method，which combines LoRA and KD in one. Training data is sampling from BAAI/Infinity-Instruct for 100k lines.
+We fine-tune `nvidia/Llama-3.1-Minitron-4B-Depth-Base` with the LLM-Neo method, which combines LoRA and KD. Training data is sampled from `BAAI/Infinity-Instruct` for 100k lines.
+
+This repository contains the model described in the paper [LLM-Neo: Parameter Efficient Knowledge Distillation for Large Language Models](https://hf.co/papers/2411.06839).
+The project page is available [here](https://huggingface.co/collections/yang31210999/llm-neo-66e3c882f5579b829ff57eba) and the Github repository is available [here](https://github.com/yang3121099/LLM-Neo).
+
+## Basic Usage
+
+This example demonstrates generating text using the model. You'll need to install the necessary libraries first: `pip install transformers`.
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
+import torch
+
+model_path = "yang31210999/Llama-3.1-Minitron-4B-Depth-Neo-10w"
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, device_map="auto", torch_dtype=torch.bfloat16)
+
+prompt = "Once upon a time"
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+generation_config = GenerationConfig(
+    max_new_tokens=50, do_sample=True, temperature=0.7
+)
 
+outputs = model.generate(**inputs, generation_config=generation_config)
+generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
+print(generated_text)
 
+```
 
-## Benchmarks 
+## Benchmarks
 
-In this section, we report the results for Llama-3.1-Minitron-4B-Depth-Neo-10w on standard automatic benchmarks. For all the evaluations, we use [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) library. 
+In this section, we report the results for `Llama-3.1-Minitron-4B-Depth-Neo-10w` on standard automatic benchmarks. For all the evaluations, we use the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) library.
 
 ### Evaluation results
 
@@ -136,4 +164,4 @@ In this section, we report the results for Llama-3.1-Minitron-4B-Depth-Neo-10w o
    <td>0.3548</td>
    <td>± 0.0044</td>
   </tr>
-</table>
+</table>