Instructions to use Nohobby/MS3-Tantum-24B-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nohobby/MS3-Tantum-24B-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="Nohobby/MS3-Tantum-24B-v0.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nohobby/MS3-Tantum-24B-v0.1")
model = AutoModelForCausalLM.from_pretrained("Nohobby/MS3-Tantum-24B-v0.1")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Nohobby/MS3-Tantum-24B-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nohobby/MS3-Tantum-24B-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nohobby/MS3-Tantum-24B-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nohobby/MS3-Tantum-24B-v0.1

SGLang

How to use Nohobby/MS3-Tantum-24B-v0.1 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 "Nohobby/MS3-Tantum-24B-v0.1" \
    --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": "Nohobby/MS3-Tantum-24B-v0.1",
		"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 "Nohobby/MS3-Tantum-24B-v0.1" \
        --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": "Nohobby/MS3-Tantum-24B-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nohobby/MS3-Tantum-24B-v0.1 with Docker Model Runner:
```
docker model run hf.co/Nohobby/MS3-Tantum-24B-v0.1
```

MS3-Tantum-24B-v0.1 / README.md

Nohobby

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	---
	language:
	- en
	license: apache-2.0
	library_name: transformers
	tags:
	- mergekit
	- merge
	base_model:
	- unsloth/Mistral-Small-24B-Base-2501
	- unsloth/Mistral-Small-24B-Instruct-2501
	- trashpanda-org/MS-24B-Instruct-Mullein-v0
	- trashpanda-org/Llama3-24B-Mullein-v1
	- ArliAI/Mistral-Small-24B-ArliAI-RPMax-v1.4
	- TheDrummer/Cydonia-24B-v2
	- estrogen/MS2501-24b-Ink-apollo-ep2
	- huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated
	- ToastyPigeon/ms3-roselily-rp-v2
	- PocketDoc/Dans-DangerousWinds-V1.1.1-24b
	- ReadyArt/Forgotten-Safeword-24B-V2.2
	- PocketDoc/Dans-PersonalityEngine-V1.2.0-24b
	- Undi95/MistralThinker-e2
	- lemonilia/Mistral-Small-3-Reasoner-s1
	- arcee-ai/Arcee-Blitz
	- SicariusSicariiStuff/Redemption_Wind_24B
	---
	***
	## Tantum

	>Everything is edible if you are brave enough

	![Так и живём](https://files.catbox.moe/ergq6n.png)

	### Overview

	It's kind of hard to judge a 24B model after using a 70B for a while. From some tests, I think it might be better than my ms-22B and qwen-32B merges.

	It has some prose, some character adherence, and... `<think>` tags! It will consistently think if you add `<think>` tag as prefill, tho I think it will obviously not think as well as an actual thinking model distill.

	People also like RP-Whole(RP-Broth). You can find it [here](https://huggingface.co/d-rang-d/MS3-RP-Broth-24B)

	Settings:

	Samplers: [Weird preset](https://files.catbox.moe/ccwmca.json) \| [Forgotten-Safeword preset](https://huggingface.co/sleepdeprived3/Mistral-V7-Tekken-Extra-Dry)

	Prompt format: Mistral-V7 (?)

	ChatML and Llama3 give better results imo. In the case of ChatML, there are Dans-PersonalityEngine and Redemption-Wind models that have been trained on it. But Llama3? No clue.

	I use [this](https://files.catbox.moe/daluze.json) lorebook for all chats instead of a system prompt for mistal models.

	### Quants

	[Static](https://huggingface.co/mradermacher/MS3-Tantum-24B-v0.1-GGUF) \| [Imatrix](https://huggingface.co/mradermacher/MS3-Tantum-24B-v0.1-i1-GGUF)

	***

	## Merge Details
	### Merging steps

	## MS3-test-Merge-1

	```yaml
	models:
	- model: unsloth/Mistral-Small-24B-Base-2501
	- model: unsloth/Mistral-Small-24B-Instruct-2501+ToastyPigeon/new-ms-rp-test-ws
	parameters:
	select_topk:
	- value: [0.05, 0.03, 0.02, 0.02, 0.01]
	- model: unsloth/Mistral-Small-24B-Instruct-2501+estrogen/MS2501-24b-Ink-ep2-adpt
	parameters:
	select_topk: 0.1
	- model: trashpanda-org/MS-24B-Instruct-Mullein-v0
	parameters:
	select_topk: 0.4
	base_model: unsloth/Mistral-Small-24B-Base-2501
	merge_method: sce
	parameters:
	int8_mask: true
	rescale: true
	normalize: true
	dtype: bfloat16
	tokenizer_source: base
	```

	```yaml
	dtype: bfloat16
	tokenizer_source: base
	merge_method: della_linear
	parameters:
	density: 0.55
	base_model: Step1
	models:
	- model: unsloth/Mistral-Small-24B-Instruct-2501
	parameters:
	weight:
	- filter: v_proj
	value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
	- filter: o_proj
	value: [1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1]
	- filter: up_proj
	value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
	- filter: gate_proj
	value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
	- filter: down_proj
	value: [1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]
	- value: 0
	- model: Step1
	parameters:
	weight:
	- filter: v_proj
	value: [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1]
	- filter: o_proj
	value: [0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0]
	- filter: up_proj
	value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
	- filter: gate_proj
	value: [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1]
	- filter: down_proj
	value: [0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1]
	- value: 1

	```

	Some early MS3 merge. Not really worth using on its own. Just added it for fun.

	## RP-half1

	```yaml
	models:
	- model: ArliAI/Mistral-Small-24B-ArliAI-RPMax-v1.4
	parameters:
	weight: 0.2
	density: 0.7
	- model: trashpanda-org/Llama3-24B-Mullein-v1
	parameters:
	weight: 0.2
	density: 0.7
	- model: TheDrummer/Cydonia-24B-v2
	parameters:
	weight: 0.2
	density: 0.7
	merge_method: della_linear
	base_model: Nohobby/MS3-test-Merge-1
	parameters:
	epsilon: 0.2
	lambda: 1.1
	dtype: bfloat16
	tokenizer:
	source: base
	```

	## RP-half2

	```yaml
	base_model: Nohobby/MS3-test-Merge-1
	parameters:
	epsilon: 0.05
	lambda: 0.9
	int8_mask: true
	rescale: true
	normalize: false
	dtype: bfloat16
	tokenizer:
	source: base
	merge_method: della
	models:
	- model: estrogen/MS2501-24b-Ink-apollo-ep2
	parameters:
	weight: [0.1, -0.01, 0.1, -0.02, 0.1]
	density: [0.6, 0.4, 0.5, 0.4, 0.6]
	- model: huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated
	parameters:
	weight: [0.02, -0.01, 0.02, -0.02, 0.01]
	density: [0.45, 0.55, 0.45, 0.55, 0.45]
	- model: ToastyPigeon/ms3-roselily-rp-v2
	parameters:
	weight: [0.01, -0.02, 0.02, -0.025, 0.01]
	density: [0.45, 0.65, 0.45, 0.65, 0.45]
	- model: PocketDoc/Dans-DangerousWinds-V1.1.1-24b
	parameters:
	weight: [0.1, -0.01, 0.1, -0.02, 0.1]
	density: [0.6, 0.4, 0.5, 0.4, 0.6]
	```

	## RP-whole

	```yaml
	base_model: ReadyArt/Forgotten-Safeword-24B-V2.2
	merge_method: model_stock
	dtype: bfloat16
	models:
	- model: mergekit-community/MS3-RP-half1
	- model: mergekit-community/MS3-RP-RP-half2
	```

	## INT

	```yaml
	merge_method: della_linear
	dtype: bfloat16
	parameters:
	normalize: true
	int8_mask: true
	tokenizer:
	source: base
	base_model: PocketDoc/Dans-PersonalityEngine-V1.2.0-24b
	models:
	- model: PocketDoc/Dans-PersonalityEngine-V1.2.0-24b
	parameters:
	density: 0.55
	weight: 1
	- model: Undi95/MistralThinker-e2
	parameters:
	density: 0.55
	weight: 1
	- model: d-rang-d/ignore_MS3-Reasoner-mergekit
	parameters:
	density: 0.55
	weight: 1
	- model: arcee-ai/Arcee-Blitz
	parameters:
	density: 0.55
	weight: 1
	```

	## Tantumv00

	```yaml
	output_base_model: "SicariusSicariiStuff/Redemption_Wind_24B"
	output_dtype: "bfloat16"
	finetune_merge:
	- { "model": "mergekit-community/MS3-INT", "base": "unsloth/Mistral-Small-24B-Instruct-2501", "alpha": 1.0, "is_input": true }
	- { "model": "mergekit-community/MS-RP-whole", "base": "unsloth/Mistral-Small-24B-Instruct-2501", "alpha": 0.7, "is_output": true }
	output_dir: "output_model"
	device: "cpu"
	clean_cache: false
	cache_dir: "cache"
	storage_dir: "storage"
	```

	Doesn't look like a mergekit recipe, right? Well, it's not. It's for a standalone merge tool: https://github.com/54rt1n/shardmerge

	If you want to use it for something non-qwen you can replace index.py with [this](https://files.catbox.moe/bgxmuz.py) and writer.py with [that](https://files.catbox.moe/ewww39.py). A much better solution is possible, ofc, but I'm a dumdum and can't code. The creator knows about this issue and will fix it... Someday, I guess.

	You also need to know that this thing is really slow, and it took me 5 hours to cram 3 24B models together.

	## Tantumv01

	```yaml
	dtype: bfloat16
	tokenizer:
	source: unsloth/Mistral-Small-24B-Instruct-2501
	merge_method: della_linear
	parameters:
	density: 0.55
	base_model: d-rang-d/MS3-megamerge
	models:
	- model: unsloth/Mistral-Small-24B-Instruct-2501
	parameters:
	weight:
	- filter: v_proj
	value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
	- filter: o_proj
	value: [1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1]
	- filter: up_proj
	value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
	- filter: gate_proj
	value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
	- filter: down_proj
	value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
	- value: 0
	- model: d-rang-d/MS3-megamerge
	parameters:
	weight:
	- filter: v_proj
	value: [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1]
	- filter: o_proj
	value: [0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0]
	- filter: up_proj
	value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
	- filter: gate_proj
	value: [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1]
	- filter: down_proj
	value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
	- value: 1
	```