Update README.md

README.md

@@ -1,3 +1,129 @@
----
-license: gpl-3.0
----
+---
+license: gpl-3.0
+datasets:
+- Mxode/BiST
+language:
+- en
+- zh
+pipeline_tag: translation
+library_name: transformers
+---
+# **NanoTranslator-L**
+
+English | [简体中文](README_zh-CN.md)
+
+## Introduction
+
+This is the **large** model of the NanoTranslator, currently supported only in **English to Chinese**.
+
+The ONNX version of the model is also available in the repository.
+
+
+| Size | P. | Arch. | Act. |  V.  |  H.  |  I.  |  L.  | A.H. | K.H. | Tie |
+| :--: | :-----: | :--: | :--: | :--: | :-----: | :---: | :------: | :----: | :----: | :--: |
+|  XL  |  100  |  LLaMA  |  SwiGLU  | 16K | 768  | 4096 |  8   |  24  |  8   | True |
+|  L   |  78  | LLaMA | GeGLU  | 16K | 768  | 4096 |  6   |  24  |  8   | True |
+| M2 | 22 | Qwen2 | GeGLU | 4K  | 432  | 2304 |  6   |  24  |  8   | True |
+|  M   |  22  |  LLaMA  |  SwiGLU  | 8K  | 256  | 1408 |  16  |  16  |  4   | True |
+|  S   | 9 | LLaMA | SwiGLU | 4K  | 168  | 896  |  16  |  12  |  4   | True |
+| XS | 2 | LLaMA | SwiGLU | 2K | 96 | 512 | 12 | 12 | 4 | True |
+
+- **P.** - Parameters (in million)
+- **V.** - vocab size
+- **H.** - hidden size
+- **I.** - intermediate size
+- **L.** - num layers
+- **A.H.** - num attention heads
+- **K.H.** - num kv heads
+- **Tie** - tie word embeddings
+
+
+
+## How to use
+
+Prompt format as follows：
+
+```
+<|im_start|> {English Text} <|endoftext|>
+```
+
+### Directly using transformers
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_path = 'Mxode/NanoTranslator-L'
+
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(model_path)
+
+def translate(text: str, model, **kwargs):
+    generation_args = dict(
+        max_new_tokens = kwargs.pop("max_new_tokens", 512),
+        do_sample = kwargs.pop("do_sample", True),
+        temperature = kwargs.pop("temperature", 0.55),
+        top_p = kwargs.pop("top_p", 0.8),
+        top_k = kwargs.pop("top_k", 40),
+        **kwargs
+    )
+
+    prompt = "<|im_start|>" + text + "<|endoftext|>"
+    model_inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
+
+    generated_ids = model.generate(model_inputs.input_ids, **generation_args)
+    generated_ids = [
+        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+    ]
+
+    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    return response
+
+text = "I love to watch my favorite TV series."
+
+response = translate(text, model, max_new_tokens=64, do_sample=False)
+print(response)
+```
+
+
+### ONNX
+
+It has been measured that reasoning with ONNX models will be **2-10 times faster** than reasoning directly with transformers models.
+
+You should switch to [onnx branch](https://huggingface.co/Mxode/NanoTranslator-L/tree/onnx) manually and download to local.
+
+reference docs:
+
+- [Export to ONNX](https://huggingface.co/docs/transformers/serialization)
+- [Inference pipelines with the ONNX Runtime accelerator](https://huggingface.co/docs/optimum/main/en/onnxruntime/usage_guides/pipelines)
+
+**Using ORTModelForCausalLM**
+
+```python
+from optimum.onnxruntime import ORTModelForCausalLM
+from transformers import AutoTokenizer
+
+model_path = "your/folder/to/onnx_model"
+
+ort_model = ORTModelForCausalLM.from_pretrained(model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+
+text = "I love to watch my favorite TV series."
+
+response = translate(text, ort_model, max_new_tokens=64, do_sample=False)
+print(response)
+```
+
+**Using pipeline**
+
+```python
+from optimum.pipelines import pipeline
+
+model_path = "your/folder/to/onnx_model"
+pipe = pipeline("text-generation", model=model_path, accelerator="ort")
+
+text = "I love to watch my favorite TV series."
+
+response = pipe(text, max_new_tokens=64, do_sample=False)
+response
+```