Update README.md

README.md

@@ -1,89 +1,103 @@
----
-license: gpl-3.0
-language:
-- en
----
-# NanoLM-1B-Instruct-v1.1
-
-
-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-1B-Instruct-v1.1. 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-1B-Instruct-v1.1'
-
-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
-
-
-prompt = "Calculate (4 - 1)^(9 - 5)"
-print(get_response(prompt, do_sample=False))
-
-"""
-The expression (4 - 1)^(9 - 5) can be simplified as follows:
-
-(4 - 1) = 3
-
-So the expression becomes 3^(9 - 5)
-
-3^(9 - 5) = 3^4
-
-3^4 = 81
-
-Therefore, (4 - 1)^(9 - 5) = 81.
-"""
+---
+license: gpl-3.0
+language:
+- en
+datasets:
+- Mxode/Magpie-Pro-10K-GPT4o-mini
+pipeline_tag: text2text-generation
+tags:
+- chemistry
+- biology
+- finance
+- legal
+- music
+- art
+- code
+- climate
+- medical
+- text-generation-inference
+---
+# NanoLM-1B-Instruct-v1.1
+
+
+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-1B-Instruct-v1.1. 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-1B-Instruct-v1.1'
+
+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
+
+
+prompt = "Calculate (4 - 1)^(9 - 5)"
+print(get_response(prompt, do_sample=False))
+
+"""
+The expression (4 - 1)^(9 - 5) can be simplified as follows:
+
+(4 - 1) = 3
+
+So the expression becomes 3^(9 - 5)
+
+3^(9 - 5) = 3^4
+
+3^4 = 81
+
+Therefore, (4 - 1)^(9 - 5) = 81.
+"""
 ```