Instructions to use LocoreMind/LocoTrainer-4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LocoreMind/LocoTrainer-4B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="LocoreMind/LocoTrainer-4B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("LocoreMind/LocoTrainer-4B-GGUF", dtype="auto")

llama-cpp-python

How to use LocoreMind/LocoTrainer-4B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="LocoreMind/LocoTrainer-4B-GGUF",
	filename="LocoTrainer-4B-F16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use LocoreMind/LocoTrainer-4B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LocoreMind/LocoTrainer-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf LocoreMind/LocoTrainer-4B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LocoreMind/LocoTrainer-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf LocoreMind/LocoTrainer-4B-GGUF: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 LocoreMind/LocoTrainer-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf LocoreMind/LocoTrainer-4B-GGUF: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 LocoreMind/LocoTrainer-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LocoreMind/LocoTrainer-4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/LocoreMind/LocoTrainer-4B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use LocoreMind/LocoTrainer-4B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LocoreMind/LocoTrainer-4B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LocoreMind/LocoTrainer-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LocoreMind/LocoTrainer-4B-GGUF:Q4_K_M

SGLang

How to use LocoreMind/LocoTrainer-4B-GGUF 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 "LocoreMind/LocoTrainer-4B-GGUF" \
    --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": "LocoreMind/LocoTrainer-4B-GGUF",
		"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 "LocoreMind/LocoTrainer-4B-GGUF" \
        --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": "LocoreMind/LocoTrainer-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use LocoreMind/LocoTrainer-4B-GGUF with Ollama:
```
ollama run hf.co/LocoreMind/LocoTrainer-4B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use LocoreMind/LocoTrainer-4B-GGUF 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 LocoreMind/LocoTrainer-4B-GGUF 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 LocoreMind/LocoTrainer-4B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for LocoreMind/LocoTrainer-4B-GGUF to start chatting

Pi new

How to use LocoreMind/LocoTrainer-4B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf LocoreMind/LocoTrainer-4B-GGUF: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": "LocoreMind/LocoTrainer-4B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use LocoreMind/LocoTrainer-4B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf LocoreMind/LocoTrainer-4B-GGUF: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 LocoreMind/LocoTrainer-4B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use LocoreMind/LocoTrainer-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/LocoreMind/LocoTrainer-4B-GGUF:Q4_K_M
```

Lemonade

How to use LocoreMind/LocoTrainer-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LocoreMind/LocoTrainer-4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LocoTrainer-4B-GGUF-Q4_K_M

List all available models

lemonade list

LocoTrainer-4B GGUF

GGUF quantized version of LocoTrainer-4B model for local inference.

Model Information

Base Model: Qwen3-4B-Instruct-2507
Distilled from: Qwen3-Coder-Next
Training Method: Knowledge Distillation (SFT)
Training Data: 361,830 samples
Max Context: 32,768 tokens
Framework: MS-SWIFT

Available Versions

Version	Size	Speed	Quality	Recommended For
F16	8.3GB	Fast	Highest	Baseline/Reference
Q8_0	4.4GB	Fast	Very High	High-quality inference
Q5_K_M	3.0GB	Medium	High	Balanced approach
Q4_K_M	2.6GB	Fast	Medium	Recommended
Q3_K_M	2.1GB	Very Fast	Medium	Resource-constrained

Quick Start

Using llama.cpp

# Download model
wget https://huggingface.co/LocoreMind/LocoTrainer-4B-GGUF/resolve/main/LocoTrainer-4B-Q4_K_M.gguf

# Start server
./llama-server -m LocoTrainer-4B-Q4_K_M.gguf --port 8080 --ctx-size 32768

Using LocoTrainer Framework

# Configure .env
export LOCOTRAINER_BASE_URL=http://localhost:8080/v1
export LOCOTRAINER_MODEL=LocoTrainer-4B

# Run
locotrainer run -q "What are the default LoRA settings in ms-swift?"

Using llama-cpp-python

from llama_cpp import Llama

llm = Llama(
    model_path="LocoTrainer-4B-Q4_K_M.gguf",
    n_gpu_layers=99,
    n_ctx=32768,
)

response = llm(
    "What is MS-SWIFT?",
    max_tokens=512,
)
print(response["choices"][0]["text"])

Performance Metrics

Tested on NVIDIA H100:

First Token Latency: ~200-300ms
Subsequent Token Speed: 50-100 tokens/sec
Memory Usage (Q4_K_M): ~10-12GB

Features

🎯 MS-SWIFT Domain Expert: Trained on MS-SWIFT documentation and codebase
🔧 Tool Calling: Supports Read, Grep, Glob, Bash, Write tools
📊 End-to-End Reports: From question to complete markdown analysis report
🏠 Local Deployment: Fully offline, zero API cost
📏 Long Context: 32K tokens support

Use Cases

Codebase analysis and documentation generation
MS-SWIFT framework Q&A
Local AI agent deployment
Offline inference applications

License

MIT

Acknowledgments

Qwen Team - Base model
MS-SWIFT - Training framework
llama.cpp - GGUF quantization and inference
Anthropic - Claude Code design inspiration

Related Resources

Downloads last month: 47

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

3-bit

4-bit

5-bit

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16-bit

View +1 variant

Model tree for LocoreMind/LocoTrainer-4B-GGUF

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