Instructions to use bitsydarel/road-freight-voice-assistant-350m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bitsydarel/road-freight-voice-assistant-350m with Transformers:

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

pipe = pipeline("text-generation", model="bitsydarel/road-freight-voice-assistant-350m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("bitsydarel/road-freight-voice-assistant-350m", dtype="auto")

llama-cpp-python

How to use bitsydarel/road-freight-voice-assistant-350m with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="bitsydarel/road-freight-voice-assistant-350m",
	filename="Q4_K_M.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 bitsydarel/road-freight-voice-assistant-350m with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf bitsydarel/road-freight-voice-assistant-350m:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf bitsydarel/road-freight-voice-assistant-350m:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf bitsydarel/road-freight-voice-assistant-350m:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf bitsydarel/road-freight-voice-assistant-350m: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 bitsydarel/road-freight-voice-assistant-350m:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bitsydarel/road-freight-voice-assistant-350m: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 bitsydarel/road-freight-voice-assistant-350m:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bitsydarel/road-freight-voice-assistant-350m:Q4_K_M

Use Docker

docker model run hf.co/bitsydarel/road-freight-voice-assistant-350m:Q4_K_M

LM Studio
Jan

vLLM

How to use bitsydarel/road-freight-voice-assistant-350m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bitsydarel/road-freight-voice-assistant-350m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bitsydarel/road-freight-voice-assistant-350m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bitsydarel/road-freight-voice-assistant-350m:Q4_K_M

SGLang

How to use bitsydarel/road-freight-voice-assistant-350m 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 "bitsydarel/road-freight-voice-assistant-350m" \
    --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": "bitsydarel/road-freight-voice-assistant-350m",
		"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 "bitsydarel/road-freight-voice-assistant-350m" \
        --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": "bitsydarel/road-freight-voice-assistant-350m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use bitsydarel/road-freight-voice-assistant-350m with Ollama:
```
ollama run hf.co/bitsydarel/road-freight-voice-assistant-350m:Q4_K_M
```

Unsloth Studio

How to use bitsydarel/road-freight-voice-assistant-350m 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 bitsydarel/road-freight-voice-assistant-350m 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 bitsydarel/road-freight-voice-assistant-350m to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for bitsydarel/road-freight-voice-assistant-350m to start chatting

How to use bitsydarel/road-freight-voice-assistant-350m with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf bitsydarel/road-freight-voice-assistant-350m:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "bitsydarel/road-freight-voice-assistant-350m:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use bitsydarel/road-freight-voice-assistant-350m with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf bitsydarel/road-freight-voice-assistant-350m: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 bitsydarel/road-freight-voice-assistant-350m:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use bitsydarel/road-freight-voice-assistant-350m with Docker Model Runner:
```
docker model run hf.co/bitsydarel/road-freight-voice-assistant-350m:Q4_K_M
```

Lemonade

How to use bitsydarel/road-freight-voice-assistant-350m with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bitsydarel/road-freight-voice-assistant-350m:Q4_K_M

Run and chat with the model

lemonade run user.road-freight-voice-assistant-350m-Q4_K_M

List all available models

lemonade list

Road Freight Voice Assistant 350M

Fine-tuned 350M model for road-freight voice workflows in mobile apps. It is built for app tasks like navigation, load search, quoting, form repair, instruction following, and skill selection.

Intended use

Use this model inside a road-freight app, with the app supplying its tool definitions, current screen state, and confirmation rules. It is not a standalone assistant, route planner, pricing authority, or live operations record.

Give the model live app state: available vehicles, load cards, load details, quote state, current location, and whether the driver has confirmed an action. The app still validates every tool call and keeps final submission behind its own confirmation checks.

Quick start

For the BDAIAssistant SDK, use the .small model family. The SDK defaults that family to Q4_K_M.gguf, the recommended mobile file.

let spec = Lfm25GgufFileSpecification(family: .small, variant: .q4km)
let path = try await Lfm25LlamaCppLLMDownloader().downloadGgufFile(spec)

To download the GGUF directly:

hf download bitsydarel/road-freight-voice-assistant-350m Q4_K_M.gguf

Files

fused/: Hugging Face-format fused model.
model.gguf: F16 GGUF exported from the fused model.
Q8_0.gguf, Q6_K.gguf, Q5_K_M.gguf, Q5_K_S.gguf, Q4_K_M.gguf, Q4_K_S.gguf: available GGUF quantizations.
quantization.manifest.json: SHA-256 hashes and tensor-contract validation results for the GGUF files.

For mobile deployments, start with Q4_K_M.gguf if the package size works for your app. Use Q4_K_S.gguf when size matters more. Keep Q8_0.gguf for comparison checks, and use model.gguf if you need to re-export or quantize again.

This release does not publish BF16.gguf or Q4_0.gguf. The BDAIAssistant SDK treats those variants as unavailable for the .small family and fails before trying to download them.

Validation

The GGUF files listed above were regenerated from the fused model and passed the runtime tensor contract checks, including rank-2 shortconv tensors and token embedding dimensions.

License and base model

This release is a fine-tune of LiquidAI/LFM2.5-350M and follows the upstream lfm1.0 license linked in the repository metadata.

Downloads last month: 865

GGUF

Model size

0.4B params

Architecture

lfm2

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

View +1 variant

Model tree for bitsydarel/road-freight-voice-assistant-350m

Base model

LiquidAI/LFM2.5-350M-Base

Finetuned

LiquidAI/LFM2.5-350M

Quantized

(33)

this model