base_model: appvoid/palmer-002
datasets:
- appvoid/no-prompt-15k
inference: false
language:
- en
license: apache-2.0
model_creator: appvoid
model_name: palmer-002
pipeline_tag: text-generation
quantized_by: afrideva
tags:
- gguf
- ggml
- quantized
- q2_k
- q3_k_m
- q4_k_m
- q5_k_m
- q6_k
- q8_0
appvoid/palmer-002-GGUF
Quantized GGUF model files for palmer-002 from appvoid
| Name | Quant method | Size |
|---|---|---|
| palmer-002.fp16.gguf | fp16 | 2.20 GB |
| palmer-002.q2_k.gguf | q2_k | 483.12 MB |
| palmer-002.q3_k_m.gguf | q3_k_m | 550.82 MB |
| palmer-002.q4_k_m.gguf | q4_k_m | 668.79 MB |
| palmer-002.q5_k_m.gguf | q5_k_m | 783.02 MB |
| palmer-002.q6_k.gguf | q6_k | 904.39 MB |
| palmer-002.q8_0.gguf | q8_0 | 1.17 GB |
Original Model Card:
palmer
a better base model
palmer is a series of ~1b parameters language models fine-tuned to be used as base models instead of using custom prompts for tasks. This means that it can be further fine-tuned on more data with custom prompts as usual or be used for downstream tasks as any base model you can get. The model has the best of both worlds: some "bias" to act as an assistant, but also the abillity to predict the next-word from its internet knowledge base. It's a 1.1b llama 2 model so you can use it with your favorite tools/frameworks.
evaluation
| Model | ARC_C | HellaSwag | PIQA | Winogrande |
|---|---|---|---|---|
| tinyllama-2t | 0.2807 | 0.5463 | 0.7067 | 0.5683 |
| palmer-001 | 0.2807 | 0.5524 | 0.7106 | 0.5896 |
| tinyllama-2.5t | 0.3191 | 0.5896 | 0.7307 | 0.5872 |
| palmer-002 | 0.3242 | 0.5956 | 0.7345 | 0.5888 |
training
Training took ~3.5 P100 gpu hours. It was trained on 15,000 gpt-4 shuffled samples. palmer was fine-tuned using lower learning rates ensuring it keeps as much general knowledge as possible.
prompt
no prompt
