Create RedPajama-3B.md

docs/finetuning-RedPajama-3B.md

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+# RedPajama-3B
+
+In this tutorial, you will learn how to fine-tune a base LLM on a sample of data. By the end of 
+the tutorial, you will have fine-tuned the RedPajama-INCITE-Chat-3B model using a sample of 
+chat data from the OIG dataset. You can adapt this tutorial to fine-tune with your own data.
+
+In order to fine-tune the RedPajama 3B models, please follow these steps:
+
+First clone the OpenChatKit repo:
+
+```shell
+git clone git@github.com:togethercomputer/OpenChatKit.git
+```
+
+Next install dependencies as instructed by the OpenChatKit repo.
+
+# Prepare Weights
+
+```shell
+python pretrained/RedPajama-3B/prepare.py
+```
+
+This script will download the weight from HuggingFace and prepare it for finetuning. The prepared weights will be saved at 
+
+```
+pretrained/RedPajama-3B/togethercomputer_RedPajama-INCITE-Chat-3B-v1
+```
+
+# Prepare Fine Tuning Data
+
+We now need to preapre the training data.  We provide an example script that downloads a small slice of data from OIG. 
+To download this sample dataset, please run:
+
+```
+bash data/OIG-chip2/prepare.sh
+````
+
+The sample dataset will be saved at 
+
+```
+data/OIG-chip2/unified_chip2.jsonl.
+```
+
+# Run Fine Tuning Script
+
+We provide an example training script.  Please configure the parameters (e.g., learning_rate, batch_size, dataset_path) according to your hardware configuration. 
+Then to start training, simply run
+
+```
+bash training/finetune_RedPajama-INCITE-Chat-3B-v1.sh
+```
+
+# Convert to Huggingface Format
+
+The fine-tuned model will be saved to 
+
+```
+model_ckpts/rp-incite-chat-3b-finetuned/checkpoint_{steps}
+```
+
+In order to use it for inference, you will need to convert it to the HuggingFace format. To do so, run the following script 
+(as an example, please change the checkpoint path, n-stages and n-layer-per-stage according to the training script):
+
+The default for n-stages used in the training script is 10 and the n-layer-per-stage is 8.
+
+```
+python tools/convert_to_hf_gptneox.py --config-name togethercomputer/RedPajama-INCITE-Chat-3B-v1 --ckpt-path model_ckpts/redpajama-incite-chat-3b-sample/checkpoint_10/ --save-path model_ckpts/hf --n-stages 4 --n-layer-per-stage 8
+```
+
+Then you are ready to go! You can load the model with HuggingFace and use it for inference, for example:
+
+```python
+import torch
+import transformers
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-3B-v1")
+model = AutoModelForCausalLM.from_pretrained("./model_ckpts/hf", torch_dtype=torch.float16)
+model = model.to('cuda:0')
+
+prompt = "<human>: Who is Alan Turing?\n<bot>:"
+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)
+
+```