Instructions to use adelwolf5/YAM-AI-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use adelwolf5/YAM-AI-4B with Transformers:

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

pipe = pipeline("text-generation", model="adelwolf5/YAM-AI-4B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("adelwolf5/YAM-AI-4B")
model = AutoModelForMultimodalLM.from_pretrained("adelwolf5/YAM-AI-4B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use adelwolf5/YAM-AI-4B with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf adelwolf5/YAM-AI-4B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf adelwolf5/YAM-AI-4B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf adelwolf5/YAM-AI-4B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf adelwolf5/YAM-AI-4B:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf adelwolf5/YAM-AI-4B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf adelwolf5/YAM-AI-4B:Q4_K_M

Use Docker

docker model run hf.co/adelwolf5/YAM-AI-4B:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use adelwolf5/YAM-AI-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "adelwolf5/YAM-AI-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "adelwolf5/YAM-AI-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/adelwolf5/YAM-AI-4B:Q4_K_M

SGLang

How to use adelwolf5/YAM-AI-4B 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 "adelwolf5/YAM-AI-4B" \
    --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": "adelwolf5/YAM-AI-4B",
		"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 "adelwolf5/YAM-AI-4B" \
        --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": "adelwolf5/YAM-AI-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use adelwolf5/YAM-AI-4B with Ollama:
```
ollama run hf.co/adelwolf5/YAM-AI-4B:Q4_K_M
```
Unsloth Desktop

How to use adelwolf5/YAM-AI-4B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf adelwolf5/YAM-AI-4B:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "adelwolf5/YAM-AI-4B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use adelwolf5/YAM-AI-4B with Docker Model Runner:
```
docker model run hf.co/adelwolf5/YAM-AI-4B:Q4_K_M
```

Lemonade

How to use adelwolf5/YAM-AI-4B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull adelwolf5/YAM-AI-4B:Q4_K_M

Run and chat with the model

lemonade run user.YAM-AI-4B-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use adelwolf5/YAM-AI-4B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf adelwolf5/YAM-AI-4B:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default adelwolf5/YAM-AI-4B:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use adelwolf5/YAM-AI-4B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf adelwolf5/YAM-AI-4B:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "adelwolf5/YAM-AI-4B:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

YAM-AI-4B / README.md

adelwolf5

Update README.md

e3d1769 verified about 2 months ago

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---
base_model: google/gemma-4-E4B-it
library_name: transformers
pipeline_tag: text-generation
tags:
  - gemma4
  - transformers
  - lora
  - multilingual
  - yam-ai
---
# YAM AI 4B 🤖🌍

YAM AI 4B is a multilingual assistant based on **google/gemma-4-E4B-it**.

## About

- Base model: `google/gemma-4-E4B-it`
- Fine-tuned with LoRA
- LoRA merged into the base model
- Multilingual identity training across 80 languages
- Tested successfully in French, English, German, Arabic, Spanish, Italian, Russian, Japanese, Chinese, Korean and Kabyle

## Identity

**User**

```text
Who are you?
```

**YAM AI 4B**

```text
I am YAM AI, an intelligent assistant.
```

## Usage

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "adelwolf5/YAM-AI-4B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    dtype=torch.bfloat16,
    device_map="auto"
)
messages = [
    {"role": "user", "content": "Who are you?"}
]
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
inputs = tokenizer(
    prompt,
    return_tensors="pt",
    add_special_tokens=False
).to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=80
)
new_tokens = outputs[0][inputs["input_ids"].shape[1]:]
print(tokenizer.decode(new_tokens, skip_special_tokens=True))
```

## Base model

YAM AI 4B is a derivative model based on **Gemma 4 by Google**.

It was not trained from scratch.

	---
	base_model: google/gemma-4-E4B-it
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- gemma4
	- transformers
	- lora
	- multilingual
	- yam-ai
	---

	# YAM AI 4B 🤖🌍

	YAM AI 4B is a multilingual assistant based on google/gemma-4-E4B-it.

	## About

	- Base model: `google/gemma-4-E4B-it`
	- Fine-tuned with LoRA
	- LoRA merged into the base model
	- Multilingual identity training across 80 languages
	- Tested successfully in French, English, German, Arabic, Spanish, Italian, Russian, Japanese, Chinese, Korean and Kabyle

	## Identity

	User

	```text
	Who are you?
	```

	YAM AI 4B

	```text
	I am YAM AI, an intelligent assistant.
	```

	## Usage

	```python
	import torch
	from transformers import AutoTokenizer, AutoModelForCausalLM

	model_id = "adelwolf5/YAM-AI-4B"

	tokenizer = AutoTokenizer.from_pretrained(model_id)

	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	dtype=torch.bfloat16,
	device_map="auto"
	)

	messages = [
	{"role": "user", "content": "Who are you?"}
	]

	prompt = tokenizer.apply_chat_template(
	messages,
	tokenize=False,
	add_generation_prompt=True
	)

	inputs = tokenizer(
	prompt,
	return_tensors="pt",
	add_special_tokens=False
	).to(model.device)

	outputs = model.generate(
	**inputs,
	max_new_tokens=80
	)

	new_tokens = outputs[0][inputs["input_ids"].shape[1]:]

	print(tokenizer.decode(new_tokens, skip_special_tokens=True))
	```

	## Base model

	YAM AI 4B is a derivative model based on Gemma 4 by Google.

	It was not trained from scratch.