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RichardErkhov/Corianas_-_TinyTask-minipaca-gguf
Corianas_-_TinyTask-minipaca-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Downloads · 30 days
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.gguf1.2 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
TinyTask-minipaca - GGUF
| Name | Quant method | Size |
|---|---|---|
| TinyTask-minipaca.Q2_K.gguf | Q2_K | 0.03GB |
| TinyTask-minipaca.IQ3_XS.gguf | IQ3_XS | 0.03GB |
| TinyTask-minipaca.IQ3_S.gguf | IQ3_S | 0.04GB |
| TinyTask-minipaca.Q3_K_S.gguf | Q3_K_S | 0.04GB |
| TinyTask-minipaca.IQ3_M.gguf | IQ3_M | 0.04GB |
| TinyTask-minipaca.Q3_K.gguf | Q3_K | 0.04GB |
| TinyTask-minipaca.Q3_K_M.gguf | Q3_K_M | 0.04GB |
| TinyTask-minipaca.Q3_K_L.gguf | Q3_K_L | 0.04GB |
| TinyTask-minipaca.IQ4_XS.gguf | IQ4_XS | 0.04GB |
| TinyTask-minipaca.Q4_0.gguf | Q4_0 | 0.05GB |
| TinyTask-minipaca.IQ4_NL.gguf | IQ4_NL | 0.05GB |
| TinyTask-minipaca.Q4_K_S.gguf | Q4_K_S | 0.05GB |
| TinyTask-minipaca.Q4_K.gguf | Q4_K | 0.05GB |
| TinyTask-minipaca.Q4_K_M.gguf | Q4_K_M | 0.05GB |
| TinyTask-minipaca.Q4_1.gguf | Q4_1 | 0.05GB |
| TinyTask-minipaca.Q5_0.gguf | Q5_0 | 0.06GB |
| TinyTask-minipaca.Q5_K_S.gguf | Q5_K_S | 0.06GB |
| TinyTask-minipaca.Q5_K.gguf | Q5_K | 0.06GB |
| TinyTask-minipaca.Q5_K_M.gguf | Q5_K_M | 0.06GB |
| TinyTask-minipaca.Q5_1.gguf | Q5_1 | 0.06GB |
| TinyTask-minipaca.Q6_K.gguf | Q6_K | 0.07GB |
| TinyTask-minipaca.Q8_0.gguf | Q8_0 | 0.09GB |
A llama.c model based on Karpathy's Llama2.c project. https://github.com/karpathy/llama2.c
Vocab of 4096, trained on Tinystories, and my custom littlestories dataset (currently unreleased.)
This version was further trained on following instructions... somewhat... using https://github.com/mlabonne/llm-course/blob/main/Fine_tune_Llama_2_in_Google_Colab.ipynb
Model uses ↨ as a shift key, instead of using capial letters, this allowed simplification of the tokenizer to avoid duplicates that are uppercase.
To convert normal text to the right format I use:
def add_caseifer(text):
# Using list comprehension for more efficient concatenation
return ''.join(['↨' + char.lower() if char.isupper() else char for char in text])
To return the text to human format I use:
def remove_caseifer(text):
new_text = ""
i = 0
while i < len(text):
if text[i] == "↨":
if i+1 < len(text):
new_text += text[i+1].upper()
i += 1
else:
pass # skip this index
else:
new_text += text[i]
i += 1
return new_text