Instructions to use grimjim/infinite-lemonade-SLERP-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use grimjim/infinite-lemonade-SLERP-7B with Transformers:

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

pipe = pipeline("text-generation", model="grimjim/infinite-lemonade-SLERP-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("grimjim/infinite-lemonade-SLERP-7B")
model = AutoModelForCausalLM.from_pretrained("grimjim/infinite-lemonade-SLERP-7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use grimjim/infinite-lemonade-SLERP-7B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/grimjim/infinite-lemonade-SLERP-7B

SGLang

How to use grimjim/infinite-lemonade-SLERP-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 "grimjim/infinite-lemonade-SLERP-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": "grimjim/infinite-lemonade-SLERP-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 "grimjim/infinite-lemonade-SLERP-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": "grimjim/infinite-lemonade-SLERP-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

infinite-lemonade-SLERP-7B

This is a straightforward merge of two RP-oriented models.

Also available as a Q8_0 GGUF quant.

Lightly tested with Alpaca format prompts, temperature 1, and minP 0.01. Although the model claims to have a 32K context window, I would expect degradation once 8-9K is exceeded.

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

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: Endevor/InfinityRP-v1-7B
        layer_range: [0, 32]
      - model: KatyTheCutie/LemonadeRP-4.5.3
        layer_range: [0, 32]
# or, the equivalent models: syntax:
# models:
merge_method: slerp
base_model: KatyTheCutie/LemonadeRP-4.5.3
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: float16

Downloads last month: 8

Safetensors

Model size

7B params

Tensor type

F16

Model tree for grimjim/infinite-lemonade-SLERP-7B

Endevor/InfinityRP-v1-7B

KatyTheCutie/LemonadeRP-4.5.3

Merge model

this model

Quantizations

3 models