Instructions to use org2ai/Wald-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use org2ai/Wald-4B with Transformers:

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

pipe = pipeline("text-generation", model="org2ai/Wald-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("org2ai/Wald-4B")
model = AutoModelForCausalLM.from_pretrained("org2ai/Wald-4B", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Laya

How to use org2ai/Wald-4B with Laya:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use org2ai/Wald-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 org2ai/Wald-4B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf org2ai/Wald-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 org2ai/Wald-4B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf org2ai/Wald-4B:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf org2ai/Wald-4B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf org2ai/Wald-4B:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf org2ai/Wald-4B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf org2ai/Wald-4B:Q4_K_M

Use Docker

docker model run hf.co/org2ai/Wald-4B:Q4_K_M

LM Studio
Jan

vLLM

How to use org2ai/Wald-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "org2ai/Wald-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": "org2ai/Wald-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/org2ai/Wald-4B:Q4_K_M

SGLang

How to use org2ai/Wald-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 "org2ai/Wald-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": "org2ai/Wald-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 "org2ai/Wald-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": "org2ai/Wald-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use org2ai/Wald-4B with Ollama:
```
ollama run hf.co/org2ai/Wald-4B:Q4_K_M
```
Unsloth Desktop

How to use org2ai/Wald-4B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf org2ai/Wald-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": "org2ai/Wald-4B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use org2ai/Wald-4B with Docker Model Runner:
```
docker model run hf.co/org2ai/Wald-4B:Q4_K_M
```

Lemonade

How to use org2ai/Wald-4B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull org2ai/Wald-4B:Q4_K_M

Run and chat with the model

lemonade run user.Wald-4B-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use org2ai/Wald-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 org2ai/Wald-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 org2ai/Wald-4B:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use org2ai/Wald-4B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf org2ai/Wald-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 "org2ai/Wald-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"

Wald-4B / evaluation /v1.2 /release-check.json

Harry19081

Model card: announce Wald-Q4B v1.2 (robustness release, revision v1.2); weights on main unchanged (v1.1)

0b72e5a verified 6 days ago

Raw History Blame Contribute Delete

3.04 kB

	{
	"schema": "wald-q4b-release-check/1",
	"release": "Wald-Q4B v1.2",
	"revision": "v1.2",
	"commit": "db376d325c9eeb884d172e8ca9f3502d104936bb",
	"date": "2026-10-01",
	"weights": {
	"hub_lfs_sha256": {
	"model-00001-of-00002.safetensors": "0b15067f769e7388bd598aabafa2ea3211a3a0c4e49e47d07c4f65c65ffc25dd",
	"model-00002-of-00002.safetensors": "7cff102b314edeb3dc5abfa7063f72ad3e888884da237601239dc3582074120a"
	},
	"equal_to_recorded_merged_checkpoint": true
	},
	"tree": {
	"files": 69,
	"bytes": 8432242541,
	"unchanged_from_main_at_1f960412": 54,
	"new_files": [
	"evaluation/v1.2/summary.json"
	]
	},
	"anonymous_download": {
	"route": "hf-mirror.com, no token",
	"files": 69,
	"manifest_entries_checked": 60,
	"manifest_sha256_mismatches": 0
	},
	"jevbench_public_231_effort_none": {
	"how": "JevBench CLI (fstandhartinger/jevbench@9ec6f15a, typesafe adapter, serial, loopback) against wald-serve 0.1.0 + vLLM 0.30.0 on one RTX 5090; effort none taken from the release serving.json (/health: effort none)",
	"pre_release_read_2026-09-30": {
	"correct": 204,
	"of": 231,
	"tiers": {
	"easy": "48/48",
	"original": "72/72",
	"hard": "84/111"
	},
	"ece": 0.045,
	"brier": 0.191,
	"generated_tokens": 0
	},
	"download_model_files_only": {
	"correct": 204,
	"of": 231,
	"tiers": {
	"easy": "48/48",
	"original": "72/72",
	"hard": "84/111"
	},
	"ece": 0.045,
	"brier": 0.191,
	"generated_tokens": 0,
	"same_option_as_pre_release_read": 231,
	"max_abs_probability_difference": 0.0083,
	"directory": "only config, tokenizer, chat template, the two weight shards, temperature.json and serving.json of the download"
	},
	"download_complete_directory_read_1": {
	"correct": 204,
	"of": 231,
	"tiers": {
	"easy": "48/48",
	"original": "72/72",
	"hard": "84/111"
	},
	"ece": 0.053,
	"brier": 0.191,
	"generated_tokens": 0,
	"same_option_as_pre_release_read": 229,
	"max_abs_probability_difference": 0.0271,
	"directory": "the complete downloaded repository"
	},
	"download_complete_directory_read_2": {
	"correct": 205,
	"of": 231,
	"tiers": {
	"easy": "48/48",
	"original": "72/72",
	"hard": "85/111"
	},
	"ece": 0.053,
	"brier": 0.191,
	"generated_tokens": 0,
	"same_option_as_pre_release_read": 230,
	"max_abs_probability_difference": 0.0217,
	"directory": "the complete downloaded repository, separate server start"
	},
	"note": "The model files are byte-identical in all reads. From the complete repository directory almost every probability moves slightly (mean absolute difference 0.0013) and one or two near-tied items change option; from a directory with only the model files the pre-release read is reproduced. The cause of this difference is not yet explained."
	}
	}