Upload README.md

README.md

@@ -10,10 +10,17 @@ license: apache-2.0
 
 # Swallow-MS-7b-v0.1
 
-Our Swallow-MS-7b-v0.1 model has undergone continuous pre-training from the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1), primarily with the addition of Japanese language data. **The instruction tuning version will be released soon.**
+Our Swallow-MS-7b-v0.1 model has undergone continual pre-training from the Mistral-7B-v0.1, primarily with the addition of Japanese language data. 
 
+# Model Release Updates
+
+We are excited to share the release schedule for our latest models:
+- **April 26, 2024**: Released the [Swallow-MS-7b-instruct-v0.1](https://huggingface.co/tokyotech-llm/Swallow-MS-7b-instruct-v0.1)
+- **March 11, 2024**: Released the [Swallow-MS-7b-v0.1](https://huggingface.co/tokyotech-llm/Swallow-MS-7b-v0.1)
 ![logo](./logo.png)
 
+This repository provides large language models developed by [TokyoTech-LLM](https://tokyotech-llm.github.io/).
+
 ## Model Details
 
 * **Model type**: Please refer to Mistral technical report for details on the model architecture. 
@@ -21,6 +28,9 @@ Our Swallow-MS-7b-v0.1 model has undergone continuous pre-training from the [Mis
 * **Tokenizer**: This model employs a tokenizer that features a broadened vocabulary based on Japanese data. This allows for a more efficient representation of text using fewer tokens, leading to a notably faster inference process.
 * **Contact**: swallow[at]nlp.c.titech.ac.jp 
 
+## Instruct Model Performance
+
+
 ## Base Model Performance
 
 ### Japanese tasks
@@ -133,9 +143,9 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 
 model_name = "tokyotech-llm/Swallow-MS-7b-v0.1"
-tokenizer = AutoTokenizer.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-model = AutoModelForCausalLM.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
 prompt = "東京工業大学の主なキャンパスは、"
 input_ids = tokenizer.encode(
     prompt,
@@ -164,7 +174,7 @@ The following datasets were used for continual pre-training.
 - [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)
 - [Swallow Corpus](https://chokkan.org/temp/tokyotech-llm/swallow-corpus)
 - [The Pile](https://huggingface.co/datasets/EleutherAI/pile)
- 
+
 ## Risks and Limitations
 
 The models released here are still in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations.