I'm constantly enhancing these model descriptions to provide you with the most relevant and comprehensive information

Puma-3B - GGUF

Model creator: acrastt
Original model: Puma-3B

OpenLlama is a free reimplementation of the original Llama Model which is licensed under Apache 2 license.

About GGUF format

gguf is the current file format used by the ggml library. A growing list of Software is using it and can therefore use this model. The core project making use of the ggml library is the llama.cpp project by Georgi Gerganov

Quantization variants

There is a bunch of quantized files available to cater to your specific needs. Here's how to choose the best option for you:

Legacy quants

Q4_0, Q4_1, Q5_0, Q5_1 and Q8 are legacy quantization types. Nevertheless, they are fully supported, as there are several circumstances that cause certain model not to be compatible with the modern K-quants.

Note:

Now there's a new option to use K-quants even for previously 'incompatible' models, although this involves some fallback solution that makes them not real K-quants. More details can be found in affected model descriptions. (This mainly refers to Falcon 7b and Starcoder models)

K-quants

K-quants are designed with the idea that different levels of quantization in specific parts of the model can optimize performance, file size, and memory load. So, if possible, use K-quants. With a Q6_K, you'll likely find it challenging to discern a quality difference from the original model - ask your model two times the same question and you may encounter bigger quality differences.

Original Model Card:

This is OpenLLaMA 3B V2 finetuned on ShareGPT Hyperfiltered for 1 epochs.

Prompt template:

### HUMAN:
{prompt}

### RESPONSE:
<leave a newline for the model to answer>

GGML quants available here.
GPTQ quants available here.

Note: Don't expect this model to be good, I was just starting out to finetune. So don't roast me please!

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	35.93
ARC (25-shot)	41.3
HellaSwag (10-shot)	71.85
MMLU (5-shot)	27.51
TruthfulQA (0-shot)	38.34
Winogrande (5-shot)	66.38
GSM8K (5-shot)	0.76
DROP (3-shot)	5.38

End of original Model File

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