Instructions to use jeiku/Rainbow_69_7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jeiku/Rainbow_69_7B with Transformers:

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

pipe = pipeline("text-generation", model="jeiku/Rainbow_69_7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jeiku/Rainbow_69_7B")
model = AutoModelForCausalLM.from_pretrained("jeiku/Rainbow_69_7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use jeiku/Rainbow_69_7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jeiku/Rainbow_69_7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jeiku/Rainbow_69_7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jeiku/Rainbow_69_7B

SGLang

How to use jeiku/Rainbow_69_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 "jeiku/Rainbow_69_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": "jeiku/Rainbow_69_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 "jeiku/Rainbow_69_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": "jeiku/Rainbow_69_7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jeiku/Rainbow_69_7B with Docker Model Runner:
```
docker model run hf.co/jeiku/Rainbow_69_7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

technicolor consists of the following merge, which was then merged with the below LoRAs to produce rainbow:

slices:
  - sources:
      - model: paulml/OGNO-7B
        layer_range: [0, 32]
      - model: SanjiWatsuki/Kunoichi-DPO-v2-7B
        layer_range: [0, 32]
merge_method: slerp
base_model: SanjiWatsuki/Kunoichi-DPO-v2-7B
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
dtype: bfloat16

rainbow

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the task arithmetic merge method using technicolor as a base.

Models Merged

The following models were included in the merge:

technicolor + jeiku/Theory_of_Mind_Mistral
technicolor + jeiku/Gnosis_Reformatted_Mistral
technicolor + Undi95/Mistral-7B-small_pippa_limaRP-v3-lora
technicolor + jeiku/Theory_of_Mind_Roleplay_Mistral

Configuration

The following YAML configuration was used to produce this model:

merge_method: task_arithmetic
base_model: technicolor
parameters:
  normalize: true
models:
  - model: technicolor+jeiku/Theory_of_Mind_Roleplay_Mistral
    parameters:
      weight: 1
  - model: technicolor+jeiku/Theory_of_Mind_Mistral
    parameters:
      weight: 1
  - model: technicolor+jeiku/Gnosis_Reformatted_Mistral
    parameters:
      weight: 1
  - model: technicolor+Undi95/Mistral-7B-small_pippa_limaRP-v3-lora
    parameters:
      weight: 1      
dtype: float16