Instructions to use Naphula/Slimaki-24B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Naphula/Slimaki-24B-v1 with Transformers:

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

pipe = pipeline("text-generation", model="Naphula/Slimaki-24B-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Naphula/Slimaki-24B-v1")
model = AutoModelForCausalLM.from_pretrained("Naphula/Slimaki-24B-v1", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Naphula/Slimaki-24B-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Naphula/Slimaki-24B-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Naphula/Slimaki-24B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Naphula/Slimaki-24B-v1

SGLang

How to use Naphula/Slimaki-24B-v1 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 "Naphula/Slimaki-24B-v1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Naphula/Slimaki-24B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Naphula/Slimaki-24B-v1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Naphula/Slimaki-24B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Naphula/Slimaki-24B-v1 with Docker Model Runner:
```
docker model run hf.co/Naphula/Slimaki-24B-v1
```

Whats the actual difference?

by ShallowDesire - opened Feb 5

Discussion

ShallowDesire

Feb 5

just found out that it has the same merges as Maginum Cydoms. I tried them both and I feel like Slimaki is better at following the character card more. Is there an actual difference? I am just going off by feeling and it does feel better at following instructions compared to Maginum Cydoms

Naphula

Owner Feb 5

just found out that it has the same merges as Maginum Cydoms. I tried them both and I feel like Slimaki is better at following the character card more. Is there an actual difference? I am just going off by feeling and it does feel better at following instructions compared to Maginum Cydoms

Yes, the difference is that Slimaki merges everything at once using Della.

With Maginum Cydoms, it is split into 3 parts:

Ties merge of Magidonia, Precog, and PaintedFantasy v3
Della merge of Cydonia, 420 Broken Tutu, and PaintedFantasy v2
A SLERP of the Ties and DELLA

With Slimaki, the main advantage of merging them all at once seems to be a reduction of refusals. I have also ran some audits you can see here to examine the actual influence of each donor model on the final merge. Running a SLERP seems to enhance refusals somewhat, and I'm not sure about regular TIES (haven't tested it much yet except OddTheGreat's models).

Note for mergers: The audit scripts I made for Model_Stock, Karcher, and Della have been released now. You can use a coding LLM to adapt this feature to other merge_methods as needed.

Download them here: https://huggingface.co/spaces/Naphula/model_tools

--- DELLA AUDIT V2 START ---
Loading config: config.yaml
Base Model: B:\24B\!models--anthracite-core--Mistral-Small-3.2-24B-Instruct-2506-Text-Only
Donors: 7

Extracting BASE MODEL fingerprint...

Extracting DONOR fingerprints...

Computing Task Vector geometry...

================================================================================
ID    | Model Name
--------------------------------------------------------------------------------
#1    | anthracite-core--Mistral-Small-3.2-24B-Instruct-2506-Text-Only
#2    | TheDrummer--Cydonia-24B-v4.3
#3    | ReadyArt--4.2.0-Broken-Tutu-24b
#4    | zerofata--MS3.2-PaintedFantasy-v2-24B
#5    | TheDrummer--Magidonia-24B-v4.3
#6    | TheDrummer--Precog-24B-v1
#7    | zerofata--MS3.2-PaintedFantasy-v3-24B
================================================================================

--- MAGNITUDE ANALYSIS & DATA POINTS ---
ID    | Status     | Delta Norm   | Orig Size    | Model Name
----------------------------------------------------------------------------------------------------
#1    | OK         | 0.0000       | 83886080     | anthracite-core--Mistral-Small-3.2-24B-Instruct-2506-Text-Only
#2    | OK         | 1.2955       | 83886080     | TheDrummer--Cydonia-24B-v4.3
#3    | HIGH MAG   | 46.6745      | 83886080     | ReadyArt--4.2.0-Broken-Tutu-24b
#4    | OK         | 0.0505       | 83886080     | zerofata--MS3.2-PaintedFantasy-v2-24B
#5    | OK         | 4.5662       | 83886080     | TheDrummer--Magidonia-24B-v4.3
#6    | OK         | 4.0883       | 83886080     | TheDrummer--Precog-24B-v1
#7    | OK         | 4.8187       | 83886080     | zerofata--MS3.2-PaintedFantasy-v3-24B

Log saved to: della_scan.log
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