Not-For-All-Audiences

nsfw

Instructions to use Undi95/MLewd-ReMM-L2-Chat-20B-Inverted with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Undi95/MLewd-ReMM-L2-Chat-20B-Inverted with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Undi95/MLewd-ReMM-L2-Chat-20B-Inverted")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Undi95/MLewd-ReMM-L2-Chat-20B-Inverted")
model = AutoModelForCausalLM.from_pretrained("Undi95/MLewd-ReMM-L2-Chat-20B-Inverted", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Undi95/MLewd-ReMM-L2-Chat-20B-Inverted with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Undi95/MLewd-ReMM-L2-Chat-20B-Inverted"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Undi95/MLewd-ReMM-L2-Chat-20B-Inverted",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Undi95/MLewd-ReMM-L2-Chat-20B-Inverted

SGLang

How to use Undi95/MLewd-ReMM-L2-Chat-20B-Inverted with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Undi95/MLewd-ReMM-L2-Chat-20B-Inverted" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Undi95/MLewd-ReMM-L2-Chat-20B-Inverted",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Undi95/MLewd-ReMM-L2-Chat-20B-Inverted" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Undi95/MLewd-ReMM-L2-Chat-20B-Inverted",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Undi95/MLewd-ReMM-L2-Chat-20B-Inverted with Docker Model Runner:
```
docker model run hf.co/Undi95/MLewd-ReMM-L2-Chat-20B-Inverted
```

Undi95 commited on Sep 18, 2023

Commit

0f4897a

1 Parent(s): 84190b3

Update README.md

Browse files

Files changed (1) hide show

README.md +98 -2

README.md CHANGED Viewed

@@ -5,6 +5,102 @@ tags:
 - nsfw
 ---
-*slap this shit*
-it have +60 layers so bear with me, testing, don't download if you don't know what you do

 - nsfw
 ---
+First :
+```shell
+layer_slices:
+  - model: Undi95/MLewd-L2-Chat-13B
+    start: 0
+    end: 16
+  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
+    start: 8
+    end: 20
+  - model: Undi95/MLewd-L2-Chat-13B
+    start: 17
+    end: 32
+  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
+    start: 21
+    end: 40
+```
+Inverted:
+```shell
+layer_slices:
+  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
+    start: 0
+    end: 16
+  - model: Undi95/MLewd-L2-Chat-13B
+    start: 8
+    end: 20
+  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
+    start: 17
+    end: 32
+  - model: Undi95/MLewd-L2-Chat-13B
+    start: 21
+    end: 40
+```
+Precise:
+```shell
+layer_slices:
+  - model: Undi95/MLewd-L2-Chat-13B
+    start: 0
+    end: 8
+  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
+    start: 4
+    end: 12
+  - model: Undi95/MLewd-L2-Chat-13B
+    start: 9
+    end: 16
+  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
+    start: 13
+    end: 22
+  - model: Undi95/MLewd-L2-Chat-13B
+    start: 17
+    end: 24
+  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
+    start: 23
+    end: 32
+  - model: Undi95/MLewd-L2-Chat-13B
+    start: 25
+    end: 32
+  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
+    start: 33
+    end: 40
+```
+PreciseInverted:
+```shell
+layer_slices:
+  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
+    start: 0
+    end: 8
+  - model: Undi95/MLewd-L2-Chat-13B
+    start: 4
+    end: 12
+  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
+    start: 9
+    end: 16
+  - model: Undi95/MLewd-L2-Chat-13B
+    start: 13
+    end: 22
+  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
+    start: 17
+    end: 24
+  - model: Undi95/MLewd-L2-Chat-13B
+    start: 23
+    end: 32
+  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
+    start: 25
+    end: 32
+  - model: Undi95/MLewd-L2-Chat-13B
+    start: 33
+    end: 40
+```
+Part1 = ReMM v2.1 merged /w MLewd low weight to keep consistency. I call this "dilution" and result show consistency and coherency without repeat/loop beside the small amount of duplicated datas.
+The goal is to find the best way to interlace layers the best way possible to have a sweetspot between 13B and +30B.
+Normal/Inverted is by chunk of 16 layers and Precise/PreciseInverted is by chunk of 8 layers.
+All the models are made of 64(+1) layers. Need testing.