Update README.md

README.md

@@ -3,28 +3,24 @@ license: apache-2.0
 language:
 - en
 datasets:
-- togethercomputer/RedPajama-Data-1T
+- Waterhorse/chess_data
+- anon8231489123/ShareGPT_Vicuna_unfiltered
+- OpenAssistant/oasst1
+- vicgalle/alpaca-gpt4
 ---
 
-# RedPajama-INCITE-Base-3B-v1
+# Chessgpt-Chat-v1 
 
-RedPajama-INCITE-Base-3B-v1 was developed by Together and leaders from the open-source AI community including Ontocord.ai, ETH DS3Lab, AAI CERC, Université de Montréal, MILA - Québec AI Institute, Stanford Center for Research on Foundation Models (CRFM), Stanford Hazy Research research group and LAION. 
-The training was done on 3,072 V100 GPUs provided as part of the INCITE 2023 project on Scalable Foundation Models for Transferrable Generalist AI, awarded to MILA, LAION, and EleutherAI in fall 2022, with support from the Oak Ridge Leadership Computing Facility (OLCF) and INCITE program. 
+Chessgpt-Chat-v1 is the sft-tuned model of Chessgpt-Base-v1.
 
-  - Base Model: [RedPajama-INCITE-Base-3B-v1](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-3B-v1)
-  - Instruction-tuned Version: [RedPajama-INCITE-Instruct-3B-v1](https://huggingface.co/togethercomputer/RedPajama-INCITE-Instruct-3B-v1)
-  - Chat Version: [RedPajama-INCITE-Chat-3B-v1](https://huggingface.co/togethercomputer/RedPajama-INCITE-Chat-3B-v1)
+  - Base Model: [Chessgpt-Base-v1](https://huggingface.co/Waterhorse/chessgpt-base-v1)
+  - Chat Version: [Chessgpt-Chat-v1](https://huggingface.co/Waterhorse/chessgpt-chat-v1)
 
 ## Model Details
-- **Developed by**: Together Computer.
 - **Model type**: Language Model
 - **Language(s)**: English
 - **License**: Apache 2.0
-- **Model Description**: A 2.8B parameter pretrained language model.
-
-# Quick Start
-
-Please note that the model requires `transformers` version >= 4.25.1.
+- **Model Description**: A 2.8B parameter pretrained language model in Chess.
 
 ## GPU Inference
 
@@ -41,8 +37,8 @@ MIN_TRANSFORMERS_VERSION = '4.25.1'
 assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.'
 
 # init
-tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Base-3B-v1")
-model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-Base-3B-v1", torch_dtype=torch.float16)
+tokenizer = AutoTokenizer.from_pretrained("togethercomputer/chessgpt-chat-v1")
+model = AutoModelForCausalLM.from_pretrained("togethercomputer/chessgpt-chat-v1", torch_dtype=torch.float16)
 model = model.to('cuda:0')
 
 # infer
@@ -61,136 +57,18 @@ Turing’s contributions to the development of the modern computer were made in
 """
 ```
 
-## GPU Inference in Int8
-
-To run inference with int8, please ensure you have installed accelerate and bitandbytes. You can install them with the following command:
-
-```bash
-pip install accelerate
-pip install bitsandbytes
-```
-
-Then you can run inference with int8 as follows:
-
-```python
-import torch
-import transformers
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-MIN_TRANSFORMERS_VERSION = '4.25.1'
-
-# check transformers version
-assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.'
-
-# init
-tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Base-3B-v1")
-model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-Base-3B-v1", device_map='auto', torch_dtype=torch.float16, load_in_8bit=True)
-
-# infer
-prompt = "Alan Turing is"
-inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
-input_length = inputs.input_ids.shape[1]
-outputs = model.generate(
-    **inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True
-)
-token = outputs.sequences[0, input_length:]
-output_str = tokenizer.decode(token)
-print(output_str)
-"""
-the man who cracked the Enigma code during World War II, and who was later convicted of homosexual acts. He was a brilliant mathematician, and a visionary who foresaw the computer age....
-"""
-```
-
-## CPU Inference
-
-You can run inference on CPU as follows:
-
-```python
-import torch
-import transformers
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-MIN_TRANSFORMERS_VERSION = '4.25.1'
-
-# check transformers version
-assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.'
-
-# init
-tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Base-3B-v1")
-model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-Base-3B-v1", torch_dtype=torch.bfloat16)
-# infer
-prompt = "Alan Turing is"
-inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
-input_length = inputs.input_ids.shape[1]
-outputs = model.generate(
-    **inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True
-)
-token = outputs.sequences[0, input_length:]
-output_str = tokenizer.decode(token)
-print(output_str)
-"""
-a name that is synonymous with the history of computer science. As the man who invented the Turing machine, the mathematical model that defines the limits of what can be computed, Turing is credited with the invention of the modern computer. Turing was also a mathematician and logician, and his work in these fields led to the development of the field of artificial intelligence...
-"""
-```
-
-Please note that since `LayerNormKernelImpl` is not implemented in fp16 for CPU, we use `bfloat16` for CPU inference.
-
 # Uses
 
 Excluded uses are described below.
 
-### Misuse, Malicious Use, and Out-of-Scope Use
+### Direct Use
 
-It is the responsibility of the end user to ensure that the model is used in a responsible and ethical manner.
+`chessgpt-chat-v1` is mainly for research on large language model, especially for those research about policy learning and language modeling.
 
 #### Out-of-Scope Use
 
-`RedPajama-INCITE-Base-3B-v1` is a language model and may not perform well for other use cases outside of its intended scope. 
-For example, it may not be suitable for use in safety-critical applications or for making decisions that have a significant impact on individuals or society. 
-It is important to consider the limitations of the model and to only use it for its intended purpose.
-
-#### Misuse and Malicious Use
-
-`RedPajama-INCITE-Base-3B-v1` is designed for language modeling.
-Misuse of the model, such as using it to engage in illegal or unethical activities, is strictly prohibited and goes against the principles of the project.
-
-Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
-
-- Generating fake news, misinformation, or propaganda
-- Promoting hate speech, discrimination, or violence against individuals or groups
-- Impersonating individuals or organizations without their consent
-- Engaging in cyberbullying or harassment
-- Defamatory content
-- Spamming or scamming
-- Sharing confidential or sensitive information without proper authorization
-- Violating the terms of use of the model or the data used to train it
-- Creating automated bots for malicious purposes such as spreading malware, phishing scams, or spamming
-
-## Limitations
-
-`RedPajama-INCITE-Base-3B-v1`, like other language models, has limitations that should be taken into consideration. 
-For example, the model may not always provide accurate or relevant answers, particularly for questions that are complex, ambiguous, or outside of its training data. 
-We therefore welcome contributions from individuals and organizations, and encourage collaboration towards creating a more robust and inclusive chatbot.
-
-## Training
-
-**Training Data**
-
-Please refer to [togethercomputer/RedPajama-Data-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T)
-
-**Training Procedure**
-
-- **Hardware:** 256 nodes of 6xV100 (IBM Power9), on the OLCF Summit cluster
-- **Optimizer:** Apex FusedAdam
-- **Parallelism:** Pipeline parallel 6, tensor parallel 2
-- **Gradient Accumulations**: 8 (global batch size 4M tokens)
-- **Num of Tokens:** 800B Tokens
-- **Learning rate:** 0.00016
-
-## Benchmark
-
-Please refer to our [blog post](https://together.xyz) for benchmark results.
+`chessgpt-chat-v1` is a language model trained on chess related data and may not perform well for other use cases beyond chess domain.
 
-## Community
+#### Bias, Risks, and Limitations
 
-Join us on [Together Discord](https://discord.gg/6ZVDU8tTD4)
+Just as with any language model, chessgpt-chat-v1 carries inherent limitations that necessitate careful consideration. Specifically, it may occasionally generate responses that are irrelevant or incorrect, particularly when tasked with interpreting complex or ambiguous queries. Additionally, given that its training is rooted in online data, the model may inadvertently reflect and perpetuate common online stereotypes and biases.