palmer-002-GGUF / README.md
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metadata
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

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

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