mukel commited on
Commit
bc23c40
1 Parent(s): 4394b89

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +43 -0
README.md CHANGED
@@ -1,3 +1,46 @@
1
  ---
 
2
  license: gemma
 
 
 
 
 
 
 
 
3
  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
2
+ library_name: gemma2.java
3
  license: gemma
4
+ base_model: google/gemma-2-2b-it
5
+ base_model_relation: quantized
6
+ quantized_by: mukel
7
+ tags:
8
+ - gemma2
9
+ - java
10
+ - llama3.java
11
+ - gemma2.java
12
  ---
13
+
14
+ # GGUF models for gemma2.java
15
+ Pure .gguf `Q4_0` and `Q8_0` quantizations of Gemma 2 models, ready to consume by [gemma2.java](https://github.com/mukel/gemma2.java).
16
+
17
+ In the wild, `Q8_0` quantizations are fine, but `Q4_0` quantizations are rarely pure e.g. the `output.weights` tensor is quantized with `Q6_K`, instead of `Q4_0`.
18
+ A pure `Q4_0` quantization can be generated from a high precision (F32, F16, BFLOAT16) .gguf source with the `llama-quantize` utility from llama.cpp as follows:
19
+
20
+ ```
21
+ ./llama-quantize --pure ./Gemma-2-2B-Instruct-F32.gguf ./Gemma-2-2B-Instruct-Q4_0.gguf Q4_0
22
+ ```
23
+
24
+ # Gemma Model Card
25
+
26
+ **Model Page**: [Gemma](https://ai.google.dev/gemma/docs)
27
+
28
+ This model card corresponds to the 2b instruct version the Gemma 2 model in GGUF Format.
29
+
30
+ You can also visit the model card of the [2B pretrained v2 model GGUF](https://huggingface.co/google/gemma-2b-v2-GGUF).
31
+
32
+ ## Model Information
33
+
34
+ Summary description and brief definition of inputs and outputs.
35
+
36
+ ### Description
37
+
38
+ Gemma is a family of lightweight, state-of-the-art open models from Google,
39
+ built from the same research and technology used to create the Gemini models.
40
+ They are text-to-text, decoder-only large language models, available in English,
41
+ with open weights, pre-trained variants, and instruction-tuned variants. Gemma
42
+ models are well-suited for a variety of text generation tasks, including
43
+ question answering, summarization, and reasoning. Their relatively small size
44
+ makes it possible to deploy them in environments with limited resources such as
45
+ a laptop, desktop or your own cloud infrastructure, democratizing access to
46
+ state of the art AI models and helping foster innovation for everyone.