Instructions to use VladHong/Qwen3-4B-Instruct-Alpha with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VladHong/Qwen3-4B-Instruct-Alpha with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="VladHong/Qwen3-4B-Instruct-Alpha",
	filename="Qwen3-4B-Instruct-Alpha-IQ4_XS.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use VladHong/Qwen3-4B-Instruct-Alpha with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf VladHong/Qwen3-4B-Instruct-Alpha:IQ4_XS
# Run inference directly in the terminal:
llama-cli -hf VladHong/Qwen3-4B-Instruct-Alpha:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf VladHong/Qwen3-4B-Instruct-Alpha:IQ4_XS
# Run inference directly in the terminal:
llama-cli -hf VladHong/Qwen3-4B-Instruct-Alpha:IQ4_XS

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 VladHong/Qwen3-4B-Instruct-Alpha:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf VladHong/Qwen3-4B-Instruct-Alpha:IQ4_XS

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 VladHong/Qwen3-4B-Instruct-Alpha:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf VladHong/Qwen3-4B-Instruct-Alpha:IQ4_XS

Use Docker

docker model run hf.co/VladHong/Qwen3-4B-Instruct-Alpha:IQ4_XS

LM Studio
Jan
Ollama
How to use VladHong/Qwen3-4B-Instruct-Alpha with Ollama:
```
ollama run hf.co/VladHong/Qwen3-4B-Instruct-Alpha:IQ4_XS
```

Unsloth Studio new

How to use VladHong/Qwen3-4B-Instruct-Alpha 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 VladHong/Qwen3-4B-Instruct-Alpha 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 VladHong/Qwen3-4B-Instruct-Alpha to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for VladHong/Qwen3-4B-Instruct-Alpha to start chatting

Pi new

How to use VladHong/Qwen3-4B-Instruct-Alpha with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf VladHong/Qwen3-4B-Instruct-Alpha:IQ4_XS

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": "VladHong/Qwen3-4B-Instruct-Alpha:IQ4_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use VladHong/Qwen3-4B-Instruct-Alpha with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf VladHong/Qwen3-4B-Instruct-Alpha:IQ4_XS

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 VladHong/Qwen3-4B-Instruct-Alpha:IQ4_XS

Run Hermes

hermes

Docker Model Runner
How to use VladHong/Qwen3-4B-Instruct-Alpha with Docker Model Runner:
```
docker model run hf.co/VladHong/Qwen3-4B-Instruct-Alpha:IQ4_XS
```

Lemonade

How to use VladHong/Qwen3-4B-Instruct-Alpha with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull VladHong/Qwen3-4B-Instruct-Alpha:IQ4_XS

Run and chat with the model

lemonade run user.Qwen3-4B-Instruct-Alpha-IQ4_XS

List all available models

lemonade list

Qwen3-4B Instruct Alpha

Finetuned from unsloth/Qwen3-4B-Instruct-2507 using QLoRA + Unsloth. Finance-domain specialized — only finance and investing-related examples were retained for training. <think> blocks stripped from all assistant turns.

Key Differences from NoThink-V2

Unlike NoThink-V2 which trained on the full general-purpose dataset, Alpha applies a finance/investing keyword filter before training, resulting in a smaller but domain-focused dataset. It also incorporates a custom first-party dataset (VladHong/Alpha-Instruct) alongside the TeichAI sources.

Training Data

Dataset	Raw	After Finance Filter
TeichAI/gemini-3-pro-preview-high-reasoning-250x	248	120
TeichAI/gemini-3-pro-preview-high-reasoning-1000x	1,018	671
TeichAI/claude-4.5-opus-high-reasoning-250x	250	159
TeichAI/claude-sonnet-4.5-high-reasoning-250x	247	91
TeichAI/gpt-5.2-high-reasoning-250x	249	242
VladHong/Alpha-Instruct	336	265
Total	2,348	1,548

~1,425 examples after MinHash deduplication (threshold 0.8). Finance filter covers equities, bonds, funds, crypto, macro indicators, derivatives, retirement accounts, and more.

Training Details

Parameter	Value
Method	QLoRA (4-bit NF4) + Unsloth
LoRA rank	16
LoRA alpha	16
Epochs	1
Steps	179
Batch size	2 per device × 4 gradient accumulation = 8 effective
Learning rate	1e-4 (cosine schedule)
Max seq length	1024
Optimizer	AdamW 8-bit
Hardware	Kaggle Tesla T4 (14.56 GB VRAM)
Training time	~70.6 minutes
Trainable params	33M / 4.05B (0.81%)
Peak VRAM	5.47 GB (1.66 GB for LoRA)

Training used train_on_responses_only — loss computed on assistant completions only.

Files

*.gguf — IQ4_XS quantized, ready for LM Studio / Ollama / llama.cpp
lora-adapter/ — Raw LoRA weights for merging with the base model

Usage (Ollama)

ollama run VladHong/Qwen3-4B-Instruct-Alpha

License Note

Base model is Apache 2.0. Training data includes AI-generated content and a custom first-party dataset — review upstream dataset terms before commercial use.

Downloads last month: 8

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

4-bit

Inference Providers NEW

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Model tree for VladHong/Qwen3-4B-Instruct-Alpha

Base model

Qwen/Qwen3-4B-Instruct-2507

Finetuned

unsloth/Qwen3-4B-Instruct-2507

Quantized

(31)

this model

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/

Qwen3-4B-Instruct-Alpha