WikiChat-v0.2
Training in progress model to have conversations.
The GGUFs uploaded are full FP32 precision.
Using OpenOrca GPT-4 data + cosmopedia for some extra data + dolly15k for instruct
Model Details:
- 83.59M parameters (83591800)
- 8 attention heads
- 40 layers
- 384 embeddings size
- 4096/8192/16384 context (please use 2/4x RoPE scaling, may train a 16k finetuned version later)
- Batch size 16
- llama.cpp (train-text-from-scratch)
Prompt Format (Alpaca):
Instruction: {system}
Input: {prompt}
Response: {response}
Please structure your prompts in an instruct format for maximum performance.
Training Details:
- 1x RTX 3070 8GB (Infrencing speed: 80tok/s, full GPU offload)
- 1x Ryzen 3 3700x
- 96gb RAM
- 10 iterations
- Loss Target = 2.5 to 3.0
- Approx 480 samples/1M train tokens (>0.0001 epoches)
- Training data = Refer to OpenOrca page
Notes:
The model isn't ready yet; this is to test tokenization of OpenOrca and a balance between training speed and model size
Example output:
User: What is the square root of 4?
Assistant: The square root of 4 is 2.