Instructions to use Bifrost-AI/Qwen3-Bifrost-SOL-4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Bifrost-AI/Qwen3-Bifrost-SOL-4B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="Bifrost-AI/Qwen3-Bifrost-SOL-4B-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Bifrost-AI/Qwen3-Bifrost-SOL-4B-GGUF", dtype="auto")

llama-cpp-python

How to use Bifrost-AI/Qwen3-Bifrost-SOL-4B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Bifrost-AI/Qwen3-Bifrost-SOL-4B-GGUF",
	filename="Qwen-bifrost-sol-4.0B-F16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Bifrost-AI/Qwen3-Bifrost-SOL-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 Bifrost-AI/Qwen3-Bifrost-SOL-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Bifrost-AI/Qwen3-Bifrost-SOL-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 Bifrost-AI/Qwen3-Bifrost-SOL-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Bifrost-AI/Qwen3-Bifrost-SOL-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 Bifrost-AI/Qwen3-Bifrost-SOL-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Bifrost-AI/Qwen3-Bifrost-SOL-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 Bifrost-AI/Qwen3-Bifrost-SOL-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Bifrost-AI/Qwen3-Bifrost-SOL-4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Bifrost-AI/Qwen3-Bifrost-SOL-4B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Bifrost-AI/Qwen3-Bifrost-SOL-4B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Bifrost-AI/Qwen3-Bifrost-SOL-4B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Bifrost-AI/Qwen3-Bifrost-SOL-4B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Bifrost-AI/Qwen3-Bifrost-SOL-4B-GGUF:Q4_K_M

SGLang

How to use Bifrost-AI/Qwen3-Bifrost-SOL-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 "Bifrost-AI/Qwen3-Bifrost-SOL-4B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Bifrost-AI/Qwen3-Bifrost-SOL-4B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Bifrost-AI/Qwen3-Bifrost-SOL-4B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Bifrost-AI/Qwen3-Bifrost-SOL-4B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use Bifrost-AI/Qwen3-Bifrost-SOL-4B-GGUF with Ollama:
```
ollama run hf.co/Bifrost-AI/Qwen3-Bifrost-SOL-4B-GGUF:Q4_K_M
```

Unsloth Studio

How to use Bifrost-AI/Qwen3-Bifrost-SOL-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 Bifrost-AI/Qwen3-Bifrost-SOL-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 Bifrost-AI/Qwen3-Bifrost-SOL-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 Bifrost-AI/Qwen3-Bifrost-SOL-4B-GGUF to start chatting

Docker Model Runner
How to use Bifrost-AI/Qwen3-Bifrost-SOL-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/Bifrost-AI/Qwen3-Bifrost-SOL-4B-GGUF:Q4_K_M
```

Lemonade

How to use Bifrost-AI/Qwen3-Bifrost-SOL-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Bifrost-AI/Qwen3-Bifrost-SOL-4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-Bifrost-SOL-4B-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3 Bifrost SOL 4B

This fine-tuned variant of the Qwen3 4B model was supervised fine-tuned on blockchain-specific datasets(Bifrost-AI/Solana-Vanguard-Challenge), optimized for downstream tasks in blockchain coding and smart contract development on the Solana ecosystem.

The Solana Vanguard Challenge dataset, comprising 1,000 diverse and in-depth questions, offers full-spectrum coverage of the Solana ecosystem. It spans fundamental blockchain concepts, advanced on-chain programming in Rust and the Anchor framework, client-side integration in TypeScript, detailed security strategies, and performance as well as regulatory considerations.

Qwen3 Bifrost SOL 4B is in active development with additional fine-tuning sessions, & benchmark statistics coming soon!

Provided Quants

Link	Type	Size/GB	Notes
GGUF	IQ1_S	1.1	very low quality
GGUF	IQ1_M	1.2	very low quality
GGUF	TQ1_0	1.2	very low quality
GGUF	IQ2_S	1.4	fast, lower quality
GGUF	Q2_K	1.6	fast, lower quality
GGUF	Q4_K_M	2.5	fast, recommended
GGUF	Q4_K_S	2.3	fast, recommended
GGUF	Q4_0	2.3	fast, recommended
GGUF	Q5_K_S	2.7
GGUF	Q5_K_M	2.8
GGUF	Q6_K	3.1	very good quality
GGUF	Q8_0	4.0	fast, best quality
GGUF	F16	7.7	16 bpw, highest quality

Training Session:

Time: 11 hours & 22 minutes
GPU: NVIDIA GeForce RTX 3090
Batches: 1000
Context-Size: 2043
Batch-size: 1
Learning-rate: 2e-5
Training-loss: 1.06
Eval-loss: 0.81

Dataset Composition

Total Questions: 1,000
Languages Covered:
- Rust: On-chain smart contract development, security best practices, advanced state management, CPIs, PDAs, and more.
- TypeScript: Client-side integration using @solana/web3.js, wallet adapters, Metaplex for NFT protocols, dynamic transaction composition, and front-end dApp development.
Planned Extensions:
- C# (Solnet): To be integrated later for .NET ecosystem coverage.

Disclaimer

We do not recommend using Qwen3 Bifrost SOL 4B in commercial or real-world applications without further testing and development. This current model(v1) is intended for research and development purposes. While efforts have been made to align it using SFT and DPO, it may still produce outputs that are unexpected, biased, or inaccurate. Please use responsibly.