Instructions to use udgamlabs/Udgam-384M-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use udgamlabs/Udgam-384M-Base with Transformers:

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

pipe = pipeline("text-generation", model="udgamlabs/Udgam-384M-Base")
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("udgamlabs/Udgam-384M-Base")
model = AutoModelForCausalLM.from_pretrained("udgamlabs/Udgam-384M-Base", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use udgamlabs/Udgam-384M-Base 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 udgamlabs/Udgam-384M-Base:Q8_0
# Run inference directly in the terminal:
llama cli -hf udgamlabs/Udgam-384M-Base:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf udgamlabs/Udgam-384M-Base:Q8_0
# Run inference directly in the terminal:
llama cli -hf udgamlabs/Udgam-384M-Base:Q8_0

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 udgamlabs/Udgam-384M-Base:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf udgamlabs/Udgam-384M-Base:Q8_0

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 udgamlabs/Udgam-384M-Base:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf udgamlabs/Udgam-384M-Base:Q8_0

Use Docker

docker model run hf.co/udgamlabs/Udgam-384M-Base:Q8_0

Llama new
LM Studio
Jan

vLLM

How to use udgamlabs/Udgam-384M-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "udgamlabs/Udgam-384M-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "udgamlabs/Udgam-384M-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/udgamlabs/Udgam-384M-Base:Q8_0

SGLang

How to use udgamlabs/Udgam-384M-Base 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 "udgamlabs/Udgam-384M-Base" \
    --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": "udgamlabs/Udgam-384M-Base",
		"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 "udgamlabs/Udgam-384M-Base" \
        --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": "udgamlabs/Udgam-384M-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use udgamlabs/Udgam-384M-Base with Ollama:
```
ollama run hf.co/udgamlabs/Udgam-384M-Base:Q8_0
```
Unsloth Desktop

How to use udgamlabs/Udgam-384M-Base with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf udgamlabs/Udgam-384M-Base:Q8_0

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": "udgamlabs/Udgam-384M-Base:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use udgamlabs/Udgam-384M-Base with Docker Model Runner:
```
docker model run hf.co/udgamlabs/Udgam-384M-Base:Q8_0
```

Lemonade

How to use udgamlabs/Udgam-384M-Base with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull udgamlabs/Udgam-384M-Base:Q8_0

Run and chat with the model

lemonade run user.Udgam-384M-Base-Q8_0

List all available models

lemonade list

Hermes Agent

How to use udgamlabs/Udgam-384M-Base with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf udgamlabs/Udgam-384M-Base:Q8_0

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 udgamlabs/Udgam-384M-Base:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use udgamlabs/Udgam-384M-Base with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf udgamlabs/Udgam-384M-Base:Q8_0

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 "udgamlabs/Udgam-384M-Base:Q8_0" \
  --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"

jiteshkprasad commited on 12 days ago

Commit

ad3c81d

verified ·

1 Parent(s): c68875d

Card: technical report now at udgamlabs.com/reports/udgam-384m

Browse files

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -19,7 +19,7 @@ tags:
 This is a **base (pretrained) model: it continues text.** It is not instruction-tuned and not safety-tuned, and it has
 no chat behaviour of its own. For chat and tool calling use [Udgam-384M-Chat](https://huggingface.co/UdgamLabs/Udgam-384M-Chat).
-**Model family:** [Udgam-384M-Base](https://huggingface.co/UdgamLabs/Udgam-384M-Base) (2,048 context) · [Udgam-384M-Base-4K](https://huggingface.co/UdgamLabs/Udgam-384M-Base-4K) · [Udgam-384M-Chat](https://huggingface.co/UdgamLabs/Udgam-384M-Chat) · [GGUF](https://huggingface.co/UdgamLabs/Udgam-384M-Chat-GGUF) · [MLX](https://huggingface.co/UdgamLabs/Udgam-384M-Chat-MLX) · [MLX 4-bit](https://huggingface.co/UdgamLabs/Udgam-384M-Chat-MLX-4bit) · **[Technical report](https://huggingface.co/UdgamLabs/Udgam-384M-Chat/blob/main/TECHNICAL_REPORT.md)**
 | | |
 |---|---|
@@ -84,7 +84,7 @@ Probes (greedy, raw prompts in the Gemma 4 format): a `set_timer` tool prompt gi
 ## Training
-Pretrained from scratch on roughly 8B tokens of licensed public and synthetic text, at a sequence length of 2,048. See the [technical report](https://huggingface.co/UdgamLabs/Udgam-384M-Chat/blob/main/TECHNICAL_REPORT.md) for an overview.
 ## Licence
@@ -98,7 +98,7 @@ Apache-2.0 (weights and tokenizer); see `LICENSE`. The chat template is the offi
   author       = {{Udgam Labs}},
   year         = {2026},
   howpublished = {\url{https://huggingface.co/UdgamLabs/Udgam-384M-Chat}},
-  note         = {Technical report: TECHNICAL_REPORT.md in the Udgam-384M-Chat repository}
 }
 ```

 This is a **base (pretrained) model: it continues text.** It is not instruction-tuned and not safety-tuned, and it has
 no chat behaviour of its own. For chat and tool calling use [Udgam-384M-Chat](https://huggingface.co/UdgamLabs/Udgam-384M-Chat).
+**Model family:** [Udgam-384M-Base](https://huggingface.co/UdgamLabs/Udgam-384M-Base) (2,048 context) · [Udgam-384M-Base-4K](https://huggingface.co/UdgamLabs/Udgam-384M-Base-4K) · [Udgam-384M-Chat](https://huggingface.co/UdgamLabs/Udgam-384M-Chat) · [GGUF](https://huggingface.co/UdgamLabs/Udgam-384M-Chat-GGUF) · [MLX](https://huggingface.co/UdgamLabs/Udgam-384M-Chat-MLX) · [MLX 4-bit](https://huggingface.co/UdgamLabs/Udgam-384M-Chat-MLX-4bit) · **[Technical report](https://udgamlabs.com/reports/udgam-384m)**
 | | |
 |---|---|
 ## Training
+Pretrained from scratch on roughly 8B tokens of licensed public and synthetic text, at a sequence length of 2,048. See the [technical report](https://udgamlabs.com/reports/udgam-384m) for an overview.
 ## Licence
   author       = {{Udgam Labs}},
   year         = {2026},
   howpublished = {\url{https://huggingface.co/UdgamLabs/Udgam-384M-Chat}},
+  note         = {Technical report: \url{https://udgamlabs.com/reports/udgam-384m}}
 }
 ```