Instructions to use LaboAI/LaboAI-0.4.0-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LaboAI/LaboAI-0.4.0-3B with Transformers:

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

pipe = pipeline("text-generation", model="LaboAI/LaboAI-0.4.0-3B")
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("LaboAI/LaboAI-0.4.0-3B")
model = AutoModelForCausalLM.from_pretrained("LaboAI/LaboAI-0.4.0-3B", 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 LaboAI/LaboAI-0.4.0-3B 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 LaboAI/LaboAI-0.4.0-3B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LaboAI/LaboAI-0.4.0-3B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf LaboAI/LaboAI-0.4.0-3B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LaboAI/LaboAI-0.4.0-3B: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 LaboAI/LaboAI-0.4.0-3B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf LaboAI/LaboAI-0.4.0-3B: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 LaboAI/LaboAI-0.4.0-3B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LaboAI/LaboAI-0.4.0-3B:Q4_K_M

Use Docker

docker model run hf.co/LaboAI/LaboAI-0.4.0-3B:Q4_K_M

LM Studio
Jan

vLLM

How to use LaboAI/LaboAI-0.4.0-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LaboAI/LaboAI-0.4.0-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LaboAI/LaboAI-0.4.0-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LaboAI/LaboAI-0.4.0-3B:Q4_K_M

SGLang

How to use LaboAI/LaboAI-0.4.0-3B 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 "LaboAI/LaboAI-0.4.0-3B" \
    --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": "LaboAI/LaboAI-0.4.0-3B",
		"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 "LaboAI/LaboAI-0.4.0-3B" \
        --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": "LaboAI/LaboAI-0.4.0-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use LaboAI/LaboAI-0.4.0-3B with Ollama:
```
ollama run hf.co/LaboAI/LaboAI-0.4.0-3B:Q4_K_M
```
Unsloth Desktop

How to use LaboAI/LaboAI-0.4.0-3B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf LaboAI/LaboAI-0.4.0-3B: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": "LaboAI/LaboAI-0.4.0-3B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use LaboAI/LaboAI-0.4.0-3B with Docker Model Runner:
```
docker model run hf.co/LaboAI/LaboAI-0.4.0-3B:Q4_K_M
```

Lemonade

How to use LaboAI/LaboAI-0.4.0-3B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LaboAI/LaboAI-0.4.0-3B:Q4_K_M

Run and chat with the model

lemonade run user.LaboAI-0.4.0-3B-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use LaboAI/LaboAI-0.4.0-3B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf LaboAI/LaboAI-0.4.0-3B: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 LaboAI/LaboAI-0.4.0-3B:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use LaboAI/LaboAI-0.4.0-3B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf LaboAI/LaboAI-0.4.0-3B: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 "LaboAI/LaboAI-0.4.0-3B: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"

Mmxa commited on about 13 hours ago

Commit

8463417

verified ·

1 Parent(s): 45905d1

Trained with Unsloth

Browse files

Upload model trained with Unsloth 2x faster

Files changed (4) hide show

.gitattributes +1 -0
Modelfile +54 -0
Qwen2.5-Coder-3B-Instruct.Q4_K_M.gguf +3 -0
README.md +14 -12

.gitattributes CHANGED Viewed

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text

 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text
+Qwen2.5-Coder-3B-Instruct.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text

Modelfile ADDED Viewed

	@@ -0,0 +1,54 @@

+FROM Qwen2.5-Coder-3B-Instruct.Q4_K_M.gguf
+TEMPLATE """{{- if .Suffix }}<|fim_prefix|>{{ .Prompt }}<|fim_suffix|>{{ .Suffix }}<|fim_middle|>
+{{- else if .Messages }}
+{{- if or .System .Tools }}<|im_start|>system
+{{- if .System }}
+{{ .System }}
+{{- end }}
+{{- if .Tools }}
+# Tools
+You may call one or more functions to assist with the user query.
+You are provided with function signatures within <tools></tools>:
+<tools>
+{{- range .Tools }}
+{"type": "function", "function": {{ .Function }}}
+{{- end }}
+</tools>
+For each function call, return a json object with function name and arguments within <tool_call></tool_call> with NO other text. Do not include any backticks or ```json.
+<tool_call>
+{"name": <function-name>, "arguments": <args-json-object>}
+</tool_call>
+{{- end }}<|im_end|>
+{{ end }}
+{{- range $i, $_ := .Messages }}
+{{- $last := eq (len (slice $.Messages $i)) 1 -}}
+{{- if eq .Role "user" }}<|im_start|>user
+{{ .Content }}<|im_end|>
+{{ else if eq .Role "assistant" }}<|im_start|>assistant
+{{ if .Content }}{{ .Content }}
+{{- else if .ToolCalls }}<tool_call>
+{{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
+{{ end }}</tool_call>
+{{- end }}{{ if not $last }}<|im_end|>
+{{ end }}
+{{- else if eq .Role "tool" }}<|im_start|>user
+<tool_response>
+{{ .Content }}
+</tool_response><|im_end|>
+{{ end }}
+{{- if and (ne .Role "assistant") $last }}<|im_start|>assistant
+{{ end }}
+{{- end }}
+{{- else }}
+{{- if .System }}<|im_start|>system
+{{ .System }}<|im_end|>
+{{ end }}{{ if .Prompt }}<|im_start|>user
+{{ .Prompt }}<|im_end|>
+{{ end }}<|im_start|>assistant
+{{ end }}{{ .Response }}{{ if .Response }}<|im_end|>{{ end }}"""
+SYSTEM """You are Qwen, created by Alibaba Cloud. You are a helpful assistant."""

Qwen2.5-Coder-3B-Instruct.Q4_K_M.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:264bc8c65979d862af67674a4e0fe245512176ccecae90901e5a55807bd990e7
+size 1929902656

README.md CHANGED Viewed

@@ -1,21 +1,23 @@
 ---
-base_model: unsloth/Qwen2.5-Coder-3B-Instruct-bnb-4bit
 tags:
-- text-generation-inference
-- transformers
 - unsloth
-- qwen2
-license: apache-2.0
-language:
-- en
 ---
-# Uploaded finetuned  model
-- **Developed by:** LaboAI
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/Qwen2.5-Coder-3B-Instruct-bnb-4bit
-This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

 ---
 tags:
+- gguf
+- llama.cpp
 - unsloth
 ---
+# LaboAI-0.4.0-3B : GGUF
+This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
+**Example usage**:
+- For text only LLMs:    `llama-cli -hf LaboAI/LaboAI-0.4.0-3B --jinja`
+- For multimodal models: `llama-mtmd-cli -hf LaboAI/LaboAI-0.4.0-3B --jinja`
+## Available Model files:
+- `Qwen2.5-Coder-3B-Instruct.Q4_K_M.gguf`
+## Ollama
+An Ollama Modelfile is included for easy deployment.
+This was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)