Update README.md

README.md

@@ -1,94 +1,99 @@
----
-license: gpl-3.0
-language:
-- en
----
-# NanoLM-25M-Instruct-v1
-
-
-English | [简体中文](README_zh-CN.md)
-
-
-## Introduction
-
-In order to explore the potential of small models, I have attempted to build a series of them, which are available in the [NanoLM Collections](https://huggingface.co/collections/Mxode/nanolm-66d6d75b4a69536bca2705b2).
-
-This is NanoLM-25M-Instruct-v1. The model currently supports **English only**.
-
-
-
-## Model Details
-
-| Nano LMs | Non-emb Params | Arch | Layers | Dim | Heads | Seq Len |
-| :----------: | :------------------: | :---: | :----: | :-------: | :---: | :---: |
-| **25M**         | **15M**  |   **MistralForCausalLM**     | **12**      | **312**     | **12**    | **2K** |
-| 70M         | 42M |  LlamaForCausalLM          | 12     | 576    | 9   |2K|
-| 0.3B         | 180M |  Qwen2ForCausalLM  | 12   | 896    | 14 |4K|
-| 1B     | 840M | Qwen2ForCausalLM | 18   | 1536   | 12   |4K|
-
-
-
-## How to use
-
-```python
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_path = 'Mxode/NanoLM-25M-Instruct-v1'
-
-model = AutoModelForCausalLM.from_pretrained(model_path).to('cuda:0', torch.bfloat16)
-tokenizer = AutoTokenizer.from_pretrained(model_path)
-
-
-def get_response(prompt: str, **kwargs):
-    generation_args = dict(
-        max_new_tokens = kwargs.pop("max_new_tokens", 512),
-        do_sample = kwargs.pop("do_sample", True),
-        temperature = kwargs.pop("temperature", 0.7),
-        top_p = kwargs.pop("top_p", 0.8),
-        top_k = kwargs.pop("top_k", 40),
-        **kwargs
-    )
-
-    messages = [
-        {"role": "system", "content": "You are a helpful assistant."},
-        {"role": "user", "content": prompt}
-    ]
-    text = tokenizer.apply_chat_template(
-        messages,
-        tokenize=False,
-        add_generation_prompt=True
-    )
-    model_inputs = tokenizer([text], 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
-
-
-prompt1 = "What can you do for me?"
-print(get_response(prompt1, do_sample=False))
-
-"""
-I'm so glad you asked! I'm a large language model, so I don't have personal experiences or emotions, but I can provide information and assist with tasks to help with your tasks.
-
-Here are some ways I can assist you:
-
-1. **Answer questions**: I can provide information on a wide range of topics, from science and history to entertainment and culture.
-2. **Generate text**: I can create text based on a prompt or topic, and can even help with writing tasks such as proofreading and editing.
-3. **Translate text**: I can translate text from one language to another, including popular languages such as Spanish, French, German, Chinese, and many more.
-4. **Summarize content**: I can summarize long pieces of text, such as articles or documents, into shorter, more digestible versions.
-5. **Offer suggestions**: I can provide suggestions for things like gift ideas, travel destinations, books, or movies.
-6. **Chat and converse**: I can engage in natural-sounding conversations, using context and understanding to respond to questions and statements.
-7. **Play games**: I can play simple text-based games, such as 20 Questions, Hangman, or Word Jumble.
-8. **Provide definitions**: I can define words and phrases, explaining their meanings and usage.
-9. **Offer suggestions**: I can provide suggestions for things like gift ideas, travel destinations, or books to read.
-10. **Entertain**: I can engage in fun conversations, tell jokes, and even create simple games or puzzles.
-
-Which of these methods would you like to do?
-"""
-```
+---
+license: gpl-3.0
+language:
+- en
+datasets:
+- Mxode/Magpie-Pro-10K-GPT4o-mini
+pipeline_tag: text2text-generation
+tags:
+- text-generation-inference
+---
+# NanoLM-25M-Instruct-v1
+
+
+English | [简体中文](README_zh-CN.md)
+
+
+## Introduction
+
+In order to explore the potential of small models, I have attempted to build a series of them, which are available in the [NanoLM Collections](https://huggingface.co/collections/Mxode/nanolm-66d6d75b4a69536bca2705b2).
+
+This is NanoLM-25M-Instruct-v1. The model currently supports **English only**.
+
+
+
+## Model Details
+
+| Nano LMs | Non-emb Params | Arch | Layers | Dim | Heads | Seq Len |
+| :----------: | :------------------: | :---: | :----: | :-------: | :---: | :---: |
+| **25M**         | **15M**  |   **MistralForCausalLM**     | **12**      | **312**     | **12**    | **2K** |
+| 70M         | 42M |  LlamaForCausalLM          | 12     | 576    | 9   |2K|
+| 0.3B         | 180M |  Qwen2ForCausalLM  | 12   | 896    | 14 |4K|
+| 1B     | 840M | Qwen2ForCausalLM | 18   | 1536   | 12   |4K|
+
+
+
+## How to use
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_path = 'Mxode/NanoLM-25M-Instruct-v1'
+
+model = AutoModelForCausalLM.from_pretrained(model_path).to('cuda:0', torch.bfloat16)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+
+
+def get_response(prompt: str, **kwargs):
+    generation_args = dict(
+        max_new_tokens = kwargs.pop("max_new_tokens", 512),
+        do_sample = kwargs.pop("do_sample", True),
+        temperature = kwargs.pop("temperature", 0.7),
+        top_p = kwargs.pop("top_p", 0.8),
+        top_k = kwargs.pop("top_k", 40),
+        **kwargs
+    )
+
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": prompt}
+    ]
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+    model_inputs = tokenizer([text], 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
+
+
+prompt1 = "What can you do for me?"
+print(get_response(prompt1, do_sample=False))
+
+"""
+I'm so glad you asked! I'm a large language model, so I don't have personal experiences or emotions, but I can provide information and assist with tasks to help with your tasks.
+
+Here are some ways I can assist you:
+
+1. **Answer questions**: I can provide information on a wide range of topics, from science and history to entertainment and culture.
+2. **Generate text**: I can create text based on a prompt or topic, and can even help with writing tasks such as proofreading and editing.
+3. **Translate text**: I can translate text from one language to another, including popular languages such as Spanish, French, German, Chinese, and many more.
+4. **Summarize content**: I can summarize long pieces of text, such as articles or documents, into shorter, more digestible versions.
+5. **Offer suggestions**: I can provide suggestions for things like gift ideas, travel destinations, books, or movies.
+6. **Chat and converse**: I can engage in natural-sounding conversations, using context and understanding to respond to questions and statements.
+7. **Play games**: I can play simple text-based games, such as 20 Questions, Hangman, or Word Jumble.
+8. **Provide definitions**: I can define words and phrases, explaining their meanings and usage.
+9. **Offer suggestions**: I can provide suggestions for things like gift ideas, travel destinations, or books to read.
+10. **Entertain**: I can engage in fun conversations, tell jokes, and even create simple games or puzzles.
+
+Which of these methods would you like to do?
+"""
+```