Instructions to use dispatchAI/SmolLM2-135M-Instruct-mobile with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dispatchAI/SmolLM2-135M-Instruct-mobile with Transformers:

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

pipe = pipeline("text-generation", model="dispatchAI/SmolLM2-135M-Instruct-mobile")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("dispatchAI/SmolLM2-135M-Instruct-mobile", dtype="auto")

llama-cpp-python

How to use dispatchAI/SmolLM2-135M-Instruct-mobile with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="dispatchAI/SmolLM2-135M-Instruct-mobile",
	filename="model.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use dispatchAI/SmolLM2-135M-Instruct-mobile 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 dispatchAI/SmolLM2-135M-Instruct-mobile
# Run inference directly in the terminal:
llama cli -hf dispatchAI/SmolLM2-135M-Instruct-mobile

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf dispatchAI/SmolLM2-135M-Instruct-mobile
# Run inference directly in the terminal:
llama cli -hf dispatchAI/SmolLM2-135M-Instruct-mobile

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 dispatchAI/SmolLM2-135M-Instruct-mobile
# Run inference directly in the terminal:
./llama-cli -hf dispatchAI/SmolLM2-135M-Instruct-mobile

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 dispatchAI/SmolLM2-135M-Instruct-mobile
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dispatchAI/SmolLM2-135M-Instruct-mobile

Use Docker

docker model run hf.co/dispatchAI/SmolLM2-135M-Instruct-mobile

LM Studio
Jan

vLLM

How to use dispatchAI/SmolLM2-135M-Instruct-mobile with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dispatchAI/SmolLM2-135M-Instruct-mobile"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dispatchAI/SmolLM2-135M-Instruct-mobile",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dispatchAI/SmolLM2-135M-Instruct-mobile

SGLang

How to use dispatchAI/SmolLM2-135M-Instruct-mobile 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 "dispatchAI/SmolLM2-135M-Instruct-mobile" \
    --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": "dispatchAI/SmolLM2-135M-Instruct-mobile",
		"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 "dispatchAI/SmolLM2-135M-Instruct-mobile" \
        --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": "dispatchAI/SmolLM2-135M-Instruct-mobile",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use dispatchAI/SmolLM2-135M-Instruct-mobile with Ollama:
```
ollama run hf.co/dispatchAI/SmolLM2-135M-Instruct-mobile
```

Unsloth Studio

How to use dispatchAI/SmolLM2-135M-Instruct-mobile with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for dispatchAI/SmolLM2-135M-Instruct-mobile to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for dispatchAI/SmolLM2-135M-Instruct-mobile to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for dispatchAI/SmolLM2-135M-Instruct-mobile to start chatting

Atomic Chat new
Docker Model Runner
How to use dispatchAI/SmolLM2-135M-Instruct-mobile with Docker Model Runner:
```
docker model run hf.co/dispatchAI/SmolLM2-135M-Instruct-mobile
```

Lemonade

How to use dispatchAI/SmolLM2-135M-Instruct-mobile with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dispatchAI/SmolLM2-135M-Instruct-mobile

Run and chat with the model

lemonade run user.SmolLM2-135M-Instruct-mobile-{{QUANT_TAG}}

List all available models

lemonade list

3morixd commited on 14 days ago

Commit

3ab9de5

verified ·

1 Parent(s): e5361bc

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +26 -58

README.md CHANGED Viewed

@@ -1,77 +1,45 @@
 ---
 license: apache-2.0
-language:
-  - en
-library_name: transformers
 tags:
   - mobile
-  - on-device
   - quantized
   - gguf
-  - dispatchai
 pipeline_tag: text-generation
 ---
-# SmolLM2-135M-Instruct-mobile
-A 135M parameter instruction-tuned model optimized for mobile. **101MB** GGUF file, **46.0 tokens/sec** on Snapdragon 865.
-## ✅ Verified on Real Phone Hardware
-Tested on Samsung S20 FE (Snapdragon 865, 8GB RAM, Android 13) using llama.cpp b9775:
-| Prompt | Response | Correct? | Speed |
-|--------|----------|----------|-------|
-| "The capital of France is" | "...Paris, which is a city in France" | ✅ | 46.0 t/s gen, 202.4 t/s prompt |
-| "What is 2+2?" | "2 + 2 = 4" | ✅ | — |
-| "Write a greeting" | "Welcome! I'm an AI assistant..." | ✅ | — |
-**Chat format**: `llama-3` — use `chat_format="llama-3"` in llama-cpp-python
-## Model Details
-| Attribute | Value |
-|-----------|-------|
-| **Base Model** | HuggingFaceTB/SmolLM2-135M-Instruct |
-| **Parameters** | 135M |
-| **File Size** | 101 MB |
-| **Format** | GGUF |
-| **Chat Format** | llama-3 |
-| **Phone Speed** | 46.0 tokens/sec (Snapdragon 865) |
-| **CPU Speed** | 59.7 tokens/sec (112-core x86) |
-| **License** | Apache-2.0 |
-## Usage
-### Python (llama-cpp-python)
-```python
-from llama_cpp import Llama
-llm = Llama(model_path="model.gguf", chat_format="llama-3", n_ctx=512, n_threads=4)
-response = llm.create_chat_completion(
-    messages=[{"role": "user", "content": "What is the capital of France?"}],
-    max_tokens=50,
-)
-print(response["choices"][0]["message"]["content"])
-```
-### dispatchAI SDK
-```python
-from dispatchai import load_model
-model = load_model("SmolLM2-135M-Instruct-mobile", backend="gguf")
-print(model.chat("What is the capital of France?"))
-```
-### On Android (via ADB)
 ```bash
-adb push model.gguf /data/local/tmp/
-adb shell "cd /data/local/tmp && LD_LIBRARY_PATH=/data/local/tmp ./llama-cli -m model.gguf -p 'Hello' -n 30 -t 4 -st"
 ```
-## Limitations
-- 135M params — best for simple tasks (QA, classification, short summaries)
-- May repeat on some prompts
-- English-only
-## About dispatchAI
-[dispatchAI](https://huggingface.co/dispatchAI) — Small. Mobile. Free. UAE-built.

 ---
 license: apache-2.0
+base_model: HuggingFaceTB/SmolLM2-135M-Instruct
 tags:
+  - dispatch-ai
   - mobile
   - quantized
   - gguf
+  - on-device
+  - edge-ai
+  - ultra-small
+  - featherweight
 pipeline_tag: text-generation
+language:
+  - en
+library_name: transformers
 ---
+![card_image](card_image.png)
+# SmolLM2 135M — Featherweight Mobile
+**Dispatch AI** — 135 million parameters. Smaller than a WhatsApp update. And it thinks.
+## 📱 Phone Farm Benchmark
+| Metric | Value |
+|--------|-------|
+| **Generation speed** | 22.8 t/s |
+| **Model size** | ~85 MB |
+| **Load time** | 0.3s |
+| **RAM free** | 4.5 GB |
+The lightest model in our mobile lineup. Perfect for:
+- Quick text classification on-device
+- Lightweight chat assistants
+- Edge IoT devices with limited RAM
+## 💻 Usage
 ```bash
+llama-cli -m model.gguf -p "Complete: The sky is" -t 2
 ```
+**Dispatch AI (FZE)** — Sharjah, UAE | License 10818