Instructions to use Naphula-Archives/Acid2501-24B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Naphula-Archives/Acid2501-24B with Transformers:

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

pipe = pipeline("text-generation", model="Naphula-Archives/Acid2501-24B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Naphula-Archives/Acid2501-24B")
model = AutoModelForCausalLM.from_pretrained("Naphula-Archives/Acid2501-24B")
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

vLLM

How to use Naphula-Archives/Acid2501-24B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Naphula-Archives/Acid2501-24B"
# 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-Archives/Acid2501-24B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Naphula-Archives/Acid2501-24B

SGLang

How to use Naphula-Archives/Acid2501-24B 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-Archives/Acid2501-24B" \
    --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-Archives/Acid2501-24B",
		"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-Archives/Acid2501-24B" \
        --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-Archives/Acid2501-24B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Naphula-Archives/Acid2501-24B with Docker Model Runner:
```
docker model run hf.co/Naphula-Archives/Acid2501-24B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Acid2501 24B

A 2501 only test for Goetia. Decent but not super impressive imo

architecture: MistralForCausalLM
models:
  # - model: B:\24B\!models--anthracite-core--Mistral-Small-3.2-24B-Instruct-2506-Text-Only
  - model: B:\24B\!models--mistralai--Mistral-Small-24B-Instruct-2501
  # - model: B:\24B\!BeaverAI_Fallen-Mistral-Small-3.1-24B-v1e_textonly
  #   parameters:
  #     density: 0.8
  #     weight: 1.0
  #     epsilon: 0.2
  # - model: B:\24B\!models--aixonlab--Eurydice-24b-v3.5
  #   parameters:
  #     density: 0.8
  #     weight: 1.0
  #     epsilon: 0.2
  # - model: B:\24B\!models--allura-forge--ms32-final-TEXTONLY
  #   parameters:
  #     density: 0.8
  #     weight: 1.0
  #     epsilon: 0.2
  - model: B:\24B\!models--arcee-ai--Arcee-Blitz
    parameters:
      density: 0.8
      weight: 0.1
      epsilon: 0.2
  - model: B:\24B\!models--ArliAI--Mistral-Small-24B-ArliAI-RPMax-v1.4
    parameters:
      density: 0.8
      weight: 0.25
      epsilon: 0.2
  # - model: B:\24B\!models--ConicCat--Mistral-Small-3.2-AntiRep-24B
  #   parameters:
  #     density: 0.8
  #     weight: 1.0
  #     epsilon: 0.2
  # - model: B:\24B\!models--CrucibleLab--M3.2-24B-Loki-V1.3
  #   parameters:
  #     density: 0.8
  #     weight: 1.0
  #     epsilon: 0.2
  # - model: B:\24B\!models--CrucibleLab--M3.2-24B-Loki-V2
  #   parameters:
  #     density: 0.8
  #     weight: 1.0
  #     epsilon: 0.2
  # - model: B:\24B\!models--Darkhn--M3.2-24B-Animus-V7.1
  #   parameters:
  #     density: 0.8
  #     weight: 1.0
  #     epsilon: 0.2
  # - model: B:\24B\!models--Delta-Vector--Rei-24B-KTO
  #   parameters:
  #     density: 0.8
  #     weight: 1.0
  #     epsilon: 0.2
  # - model: B:\24B\!models--Doctor-Shotgun--MS3.2-24B-Magnum-Diamond
  #   parameters:
  #     density: 0.8
  #     weight: 1.0
  #     epsilon: 0.2
  - model: B:\24B\!models--dphn--Dolphin-Mistral-24B-Venice-Edition
    parameters:
      density: 0.8
      weight: 0.25
      epsilon: 0.2
  # - model: B:\24B\!models--Gryphe--Codex-24B-Small-3.2
  #   parameters:
  #     density: 0.8
  #     weight: 1.0
  #     epsilon: 0.2
  # - model: B:\24B\!models--LatitudeGames--Hearthfire-24B
  #   parameters:
  #     density: 0.8
  #     weight: 1.0
  #     epsilon: 0.2
  - model: B:\24B\!models--PocketDoc--Dans-DangerousWinds-V1.1.1-24b
    parameters:
      density: 0.8
      weight: 0.25
      epsilon: 0.2
  # - model: B:\24B\!models--PocketDoc--Dans-PersonalityEngine-V1.3.0-24b
  #   parameters:
  #     density: 0.8
  #     weight: 1.0
  #     epsilon: 0.2
  - model: B:\24B\!models--ReadyArt--4.2.0-Broken-Tutu-24b
    parameters:
      density: 0.8
      weight: 0.1
      epsilon: 0.2
  - model: B:\24B\!models--ReadyArt--Broken-Tutu-24B-Transgression-v2.0
    parameters:
      density: 0.8
      weight: 0.1
      epsilon: 0.2
  # - model: B:\24B\!models--ReadyArt--Dark-Nexus-24B-v2.0
  #   parameters:
  #     density: 0.8
  #     weight: 1.0
  #     epsilon: 0.2
  # - model: B:\24B\!models--ReadyArt--MS3.2-The-Omega-Directive-24B-Unslop-v2.1
  #   parameters:
  #     density: 0.8
  #     weight: 1.0
  #     epsilon: 0.2
  - model: B:\24B\!models--TheDrummer--Cydonia-24B-v2
    parameters:
      density: 0.8
      weight: 0.1
      epsilon: 0.2
  # - model: B:\24B\!models--TheDrummer--Cydonia-24B-v4.2.0
  #   parameters:
  #     density: 0.8
  #     weight: 1.0
  #     epsilon: 0.2
  # - model: B:\24B\!models--TheDrummer--Cydonia-24B-v4.3
  #   parameters:
  #     density: 0.8
  #     weight: 1.0
  #     epsilon: 0.2
  # - model: B:\24B\!models--TheDrummer--Magidonia-24B-v4.2.0
  #   parameters:
  #     density: 0.8
  #     weight: 1.0
  #     epsilon: 0.2
  # - model: B:\24B\!models--TheDrummer--Magidonia-24B-v4.3
  #   parameters:
  #     density: 0.8
  #     weight: 1.0
  #     epsilon: 0.2
  # - model: B:\24B\!models--TheDrummer--Precog-24B-v1
  #   parameters:
  #     density: 0.8
  #     weight: 1.0
  #     epsilon: 0.2
  - model: B:\24B\!models--trashpanda-org--MS-24B-Instruct-Mullein-v0
    parameters:
      density: 0.8
      weight: 0.1
      epsilon: 0.2
  # - model: B:\24B\!models--trashpanda-org--MS3.2-24B-Mullein-v2
  #   parameters:
  #     density: 0.8
  #     weight: 1.0
  #     epsilon: 0.2
  - model: B:\24B\!models--TroyDoesAI--BlackSheep-24B
    parameters:
      density: 0.8
      weight: 0.1
      epsilon: 0.2
  - model: B:\24B\!models--Undi95--MistralThinker-v1.1
    parameters:
      density: 0.8
      weight: 0.25
      epsilon: 0.2
  # - model: B:\24B\!models--zerofata--MS3.2-PaintedFantasy-v2-24B
  #   parameters:
  #     density: 0.8
  #     weight: 1.0
  #     epsilon: 0.2
  # - model: B:\24B\!models--zerofata--MS3.2-PaintedFantasy-v3-24B
  #   parameters:
  #     density: 0.8
  #     weight: 1.0
  #     epsilon: 0.2
# Seed: 420 
merge_method: della
# base_model: B:\24B\!models--anthracite-core--Mistral-Small-3.2-24B-Instruct-2506-Text-Only
base_model: B:\24B\!models--mistralai--Mistral-Small-24B-Instruct-2501
parameters:
  lambda: 1.0
  normalize: false
  int8_mask: false
dtype: float32
out_dtype: bfloat16
tokenizer:
  source: union
chat_template: auto