Instructions to use Nekochu/Confluence-Renegade-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nekochu/Confluence-Renegade-7B with Transformers:

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

pipe = pipeline("text-generation", model="Nekochu/Confluence-Renegade-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nekochu/Confluence-Renegade-7B")
model = AutoModelForCausalLM.from_pretrained("Nekochu/Confluence-Renegade-7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Nekochu/Confluence-Renegade-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nekochu/Confluence-Renegade-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nekochu/Confluence-Renegade-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Nekochu/Confluence-Renegade-7B

SGLang

How to use Nekochu/Confluence-Renegade-7B 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 "Nekochu/Confluence-Renegade-7B" \
    --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": "Nekochu/Confluence-Renegade-7B",
		"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 "Nekochu/Confluence-Renegade-7B" \
        --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": "Nekochu/Confluence-Renegade-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Nekochu/Confluence-Renegade-7B with Docker Model Runner:
```
docker model run hf.co/Nekochu/Confluence-Renegade-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

My first merge of RP models 7B using mergekit, They are just r/ trend RP, half is BuRP_7B. not used any, Dumb merge but hopfully lucky merge! ^^'

Update 03/2024:

Original model Card Confluence-Renegade-7B [8.0bpw-exl]
Added Model and merge recipe branch: Confluence-Renegade-7B-v2
Added Model and merge recipe branch: RoleBeagle-Moistral-11B-v2 [7B truncated] and Quants RoleBeagle-Moistral-11B-v2-2.4bpw-h6-exl2, 4.25bpw-h6, 8.0bpw-h8
Added Branch: Confluence-Shortcake-20B Model recipes and Quants here Confluence-Shortcake-20B-2.4bpw-h6-exl2, 4.25bpw-h6, 8.0bpw-h8

Name symbolize by Confluence for many unique RP model with Renegade mostly come from no-guardrail.

Download branch instructions

git clone --single-branch --branch Confluence-Shortcake-20B-2.4bpw-h6-exl2 https://huggingface.co/Nekochu/Confluence-Renegade-7B

Configuration Confluence-Renegade-7B

The following YAML configuration was used to produce this model:

models:
  - model: ./modela/Erosumika-7B
    parameters:
      density: [1, 0.8, 0.6]
      weight: 0.2
  - model: ./modela/Infinitely-Laydiculous-7B
    parameters:
      density: [0.9, 0.7, 0.5]
      weight: 0.2
  - model: ./modela/Kunocchini-7b-128k-test
    parameters:
      density: [0.8, 0.6, 0.4]
      weight: 0.2
  - model: ./modela/EndlessRP-v3-7B
    parameters:
      density: [0.7, 0.5, 0.3]
      weight: 0.2
  - model: ./modela/daybreak-kunoichi-2dpo-7b
    parameters:
      density: [0.5, 0.3, 0.1]
      weight: 0.2
merge_method: dare_linear
base_model: ./modela/Mistral-7B-v0.1
parameters:
  normalize: true
  int8_mask: true
dtype: bfloat16
name: intermediate-model
---
slices:
  - sources:
      - model: intermediate-model
        layer_range: [0, 32]
      - model: ./modela/BuRP_7B
        layer_range: [0, 32]
merge_method: slerp
base_model: intermediate-model
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5 # fallback for rest of tensors
dtype: bfloat16
name: gradient-slerp