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To get started, create an account on Haven. You will now automatically get $5 in credits.

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Create a dataset file

Your dataset should be a jsonl file (a file containing a json object in every line), following the same format as the OpenAI fine-tuning data format. The first message should be a system message, and afterwards, roles should alternate between user and assistant: 
{"messages": [{"role": "system", "content": "Marv is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "What's the capital of France?"}, {"role": "assistant", "content": "Paris, as if everyone doesn't know that already."}]}
{"messages": [{"role": "system", "content": "Marv is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "Who wrote 'Romeo and Juliet'?"}, {"role": "assistant", "content": "Oh, just some guy named William Shakespeare. Ever heard of him?"}]}
{"messages": [{"role": "system", "content": "Marv is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "How far is the Moon from Earth?"}, {"role": "assistant", "content": "Around 384,400 kilometers. Give or take a few, like that really matters."}]}
You can download an example dataset here.

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Start a Finetuning Run

To start a finetuning run, click onto “Train a model” in Haven’s dashboard.
Now, upload your file and indicate the following parameters in the form:
  • Model Name: This is where the fine-tuned model will be uploaded to on Huggingface, should be of format your-hf-username/your-model-name
  • Training Dataset: Your training dataset file.
  • Base model: Model you want to finetune. We suggest HuggingFaceH4/zephyr-7b-beta.
  • Learning Rate: Can be high, medium or low. In general, smaller datasets with less than 500 chats should use high.
  • Number of epochs: The number of training iterations over your full dataset. This value should normally be in the range of one to five.
  • Huggingface Token: A write-access Huggingface token to upload your model. To obtain an access token, see here.
Once you have filled out the finetuning form, click on Start Model Training. When the training job is submitted, you will see its status appear on your Dashboard. You will also be able to see fine-tuning logs from Weights and Biases.

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Testing your trained Model

Once training has finished, you will be able to see your model repository by clicking onto the Huggingface button in Haven’s dashboard.
If you have access to a GPU (free instances are available on Google Colab) you can test it with the transformers / peft library: 
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("your-hf-name/your-model")
model = AutoPeftModelForCausalLM.from_pretrained("your-hf-name/your-model").to("cuda") # if you get a CUDA out of memory error, try load_in_8bit=True

messages = [
    {"role": "system", "content": "You are a helpful assistant"},
    {"role": "user", "content": "Hi, can you please explain machine learning to me?"}
]

encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
generated_ids = model.generate(input_ids=model_inputs, min_new_tokens=10, max_new_tokens=300, do_sample=True, temperature=0.9, top_p=0.8)
decoded = tokenizer.batch_decode(generated_ids)

print(decoded[0])
More efficient deployment options include:
  • Self-Hosting on AWS / GCP with vllm
  • Running the model on your laptop using llama.cpp
  • Deploying a Huggingface Inference Endpoint
  • Running on Haven (coming soon)