Instructions to use redrix/patricide-12B-Unslop-Mell with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use redrix/patricide-12B-Unslop-Mell with Transformers:

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

pipe = pipeline("text-generation", model="redrix/patricide-12B-Unslop-Mell")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("redrix/patricide-12B-Unslop-Mell")
model = AutoModelForCausalLM.from_pretrained("redrix/patricide-12B-Unslop-Mell")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use redrix/patricide-12B-Unslop-Mell with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "redrix/patricide-12B-Unslop-Mell"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "redrix/patricide-12B-Unslop-Mell",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/redrix/patricide-12B-Unslop-Mell

SGLang

How to use redrix/patricide-12B-Unslop-Mell 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 "redrix/patricide-12B-Unslop-Mell" \
    --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": "redrix/patricide-12B-Unslop-Mell",
		"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 "redrix/patricide-12B-Unslop-Mell" \
        --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": "redrix/patricide-12B-Unslop-Mell",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use redrix/patricide-12B-Unslop-Mell with Docker Model Runner:
```
docker model run hf.co/redrix/patricide-12B-Unslop-Mell
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

A newer version of this model is available: redrix/patricide-12B-Unslop-Mell-v2

patricide-12B-Unslop-Mell

The sins of the Father shan't ever be repeated this way.

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

This is my first merge — I still have no idea how writing the parameters in the config actually works. (Update: I figured it out) If anyone has more extensive guides for merging, please let me know. I would also like to get into the science behind all this.

Both models produced enjoyable results, so I decided to merge them, to create a model hopefully inheriting good traits of the parents. (Update: The early testing of this model revealed good coherency, but it sometimes spits out unintelligeble gibberish or made-up words. This is likely due to the broken tokenizer)

I've tested this model on the Q6_K GGUF Quant and it provided satisfactory results, thus I decided to upload it. Although I've not extensively tested it in Storywriting nor RP, the results were stable and at least coherent. I tested it on a Temperature of 1 (Temperature last) and Min-P of 0.1. I don't know the effects DRY or XTC have on the stability of the output, or how it fares on high context sizes. Both parent models use the ChatML Template. Although Unslop-Nemo also uses Metharme/Pygmalion. I've not yet tested which works better. (Update: Mergekit introduced a feature to define the template; I will force it to use ChatML in my next models, so it has an all-around standard.)

Feel free to experiment, as I am only experimenting myself.

Update: I will likely release my next models once I am able to run them, without too much fine-tuning of samplers/parameters/text templates/etc. Extensive testing as per DavidAU's approach will be done afterwards, so I may gain more impressions, while being able to work on new models already. I would like to create models that are very good in their base states, with samplers being the thing to perfect them. As such I won't spend too much time finetuning samplers, unless the model's base state is very promising.

Quantization

Static GGUF Quants available at:

redrix/patricide-12B-Unslop-Mell-GGUF (has less quants than below ⬇️)
mradermacher/patricide-12B-Unslop-Mell-GGUF (Thanks ♥️)

Weighted/Imatrix GGUF Quants available at mradermacher/patricide-12B-Unslop-Mell-i1-GGUF.

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:

models:
  - model: TheDrummer/UnslopNemo-12B-v4.1
  - model: inflatebot/MN-12B-Mag-Mell-R1
merge_method: slerp
base_model: TheDrummer/UnslopNemo-12B-v4.1
dtype: bfloat16
parameters:
  t: [0, 0.5, 1, 0.5, 0]

I made the cover art myself in Photoshop... I don't use AI for stuff like that.

Downloads last month: 73

Safetensors

Model size

12B params

Tensor type

BF16

Model tree for redrix/patricide-12B-Unslop-Mell

TheDrummer/UnslopNemo-12B-v4.1

inflatebot/MN-12B-Mag-Mell-R1

Merge model

this model

Finetunes

1 model

Merges

8 models

Quantizations

6 models