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

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"

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Udgam-384M-Base

A 384M-parameter English base model trained from scratch by UdgamLabs. It is a decoder-only transformer compatible with the Gemma 4 architecture (Gemma4ForCausalLM), so it runs unmodified in 🤗 transformers, llama.cpp / Ollama / LM Studio (GGUF) and MLX. It is the foundation of the on-device tool-calling model Udgam-384M-Chat. For a 4,096-token context use Udgam-384M-Base-4K.

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.

Model family: Udgam-384M-Base (2,048 context) · Udgam-384M-Base-4K · Udgam-384M-Chat · GGUF · MLX · MLX 4-bit · Technical report


Developer	UdgamLabs
Parameters	384.4M total (317.3M in the 20 transformer layers; untied 32,768 × 1,024 input and output embeddings)
Architecture	decoder-only transformer compatible with Gemma 4 (`Gemma4ForCausalLM`): 20 layers × 1,024, 16 query / 4 KV heads, sliding-window (512) and global attention in a 3 : 1 pattern, GeGLU MLP, logit soft-cap
Vocabulary	32,768 tokens, with the Gemma 4 chat-template special tokens
Context	2,048 tokens (`max_position_embeddings` 2048; GGUF `context_length` 2048)
Language	English
Training	pretrained from scratch on roughly 8B tokens of licensed public and synthetic text
Licence	Apache-2.0

How to use

from transformers import AutoTokenizer, AutoModelForCausalLM
tok = AutoTokenizer.from_pretrained("UdgamLabs/Udgam-384M-Base")
model = AutoModelForCausalLM.from_pretrained("UdgamLabs/Udgam-384M-Base")
ids = tok("<bos>The capital of France is", return_tensors="pt", add_special_tokens=False).input_ids  # add <bos> yourself
print(tok.decode(model.generate(ids, max_new_tokens=30, do_sample=False)[0]))

As with official Gemma 4, tokenizer(text) does not add <bos>; prepend it (or use the chat template).

GGUF (in gguf/: f16 and Q8_0) with llama.cpp: llama-cli -m Udgam-384M-Base-Q8_0.gguf -c 2048 -p "The capital of France is" -n 40. Ollama (raw completion):

FROM ./Udgam-384M-Base-Q8_0.gguf
TEMPLATE """{{ .Prompt }}"""
PARAMETER temperature 0.7
PARAMETER top_p 0.9
PARAMETER repeat_penalty 1.15
PARAMETER num_ctx 2048

MLX: mlx_lm.generate --model UdgamLabs/Udgam-384M-Base --ignore-chat-template --prompt "<bos>The capital of France is" (mlx-lm reads the safetensors directly). Keep the literal <bos>: without it the model degenerates ("is is is…"); without --ignore-chat-template the prompt is wrapped in the chat template and the base model rambles.

Export parity was checked: transformers fp32, llama.cpp GGUF and MLX give the same greedy tokens, and Ollama's greedy output matches transformers.

Evaluation

Held-out bits per byte at 2,048 tokens (lower is better): 0.668 on mixed validation text, 0.865 on web text, 0.408 on tool-call / JSON / chat-style text.

Probes (greedy, raw prompts in the Gemma 4 format): a set_timer tool prompt gives a correct call <|tool_call>call:set_timer{label:<|"|>pasta<|"|>,minutes:15}<tool_call|>; a shell prompt gives find /var/log -type f -size +10M; "The chemical symbol of gold is" → " Au." Typical base-model weaknesses: repetition ("The capital of France is Paris, and the capital of Belgium is Brussels. The capital of France is …"), date-reasoning errors, buggy code. The pretraining text included structured and conversational material, so the base already knows the Gemma 4 tool-call format: prompted with the chat template, the 4K base scores BFCL v3 simple 66% / multiple 75% but irrelevance only 11% (it calls a tool almost always). We did not run knowledge benchmarks (HellaSwag, ARC, MMLU); at 384M parameters and ~8B tokens expect scores in line with other sub-500M models trained on similar budgets, far below larger models.

Limitations

Small model, small token budget: limited world knowledge, frequent factual errors, repetition, date-reasoning errors and buggy code.
English only. Context 2,048 tokens (use Udgam-384M-Base-4K for longer inputs).
Not instruction-tuned or safety-tuned: it will continue any text, including harmful text. Do not deploy it directly to end users.
Prompted as a chatbot without fine-tuning, it tends to ramble in a "thinking aloud" style ("Okay, let me…", "Wait, …"). Fine-tuning (as in Udgam-384M-Chat) removes this.
Parts of the training text were machine-generated; their styles and errors can carry over.

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 for an overview.

Licence

Apache-2.0 (weights and tokenizer); see LICENSE. The chat template is the official Gemma 4 template (Apache-2.0).

Citation

@misc{udgamlabs2026udgam384m,
  title        = {Udgam-384M: a small on-device tool-calling model trained from scratch},
  author       = {{UdgamLabs}},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/UdgamLabs/Udgam-384M-Chat}},
  note         = {Technical report: TECHNICAL_REPORT.md in the Udgam-384M-Chat repository}
}

Contact

UdgamLabs. Please use the Community (discussions) tab of the model page for questions, issues and feedback.

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