Instructions to use Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF", dtype="auto") - llama-cpp-python
How to use Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF", filename="nextcoder-Mirage-7.6B-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 Bifrost-AI/NextCoder-Mirage-sol-7B-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/NextCoder-Mirage-sol-7B-GGUF:Q4_K_S # Run inference directly in the terminal: llama-cli -hf Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF:Q4_K_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF:Q4_K_S # Run inference directly in the terminal: llama-cli -hf Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF:Q4_K_S
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/NextCoder-Mirage-sol-7B-GGUF:Q4_K_S # Run inference directly in the terminal: ./llama-cli -hf Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF:Q4_K_S
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/NextCoder-Mirage-sol-7B-GGUF:Q4_K_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF:Q4_K_S
Use Docker
docker model run hf.co/Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF:Q4_K_S
- LM Studio
- Jan
- vLLM
How to use Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Bifrost-AI/NextCoder-Mirage-sol-7B-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": "Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF:Q4_K_S
- SGLang
How to use Bifrost-AI/NextCoder-Mirage-sol-7B-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/NextCoder-Mirage-sol-7B-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": "Bifrost-AI/NextCoder-Mirage-sol-7B-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 "Bifrost-AI/NextCoder-Mirage-sol-7B-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": "Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF with Ollama:
ollama run hf.co/Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF:Q4_K_S
- Unsloth Studio new
How to use Bifrost-AI/NextCoder-Mirage-sol-7B-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/NextCoder-Mirage-sol-7B-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/NextCoder-Mirage-sol-7B-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/NextCoder-Mirage-sol-7B-GGUF to start chatting
- Pi new
How to use Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF:Q4_K_S
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": "Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF:Q4_K_S" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Bifrost-AI/NextCoder-Mirage-sol-7B-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 Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF:Q4_K_S
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 Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF:Q4_K_S
Run Hermes
hermes
- Docker Model Runner
How to use Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF with Docker Model Runner:
docker model run hf.co/Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF:Q4_K_S
- Lemonade
How to use Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF:Q4_K_S
Run and chat with the model
lemonade run user.NextCoder-Mirage-sol-7B-GGUF-Q4_K_S
List all available models
lemonade list
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF:# Run inference directly in the terminal:
llama-cli -hf Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF: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/NextCoder-Mirage-sol-7B-GGUF:# Run inference directly in the terminal:
./llama-cli -hf Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF: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/NextCoder-Mirage-sol-7B-GGUF:# Run inference directly in the terminal:
./build/bin/llama-cli -hf Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF:Use Docker
docker model run hf.co/Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF:NextCoder Mirage SOL 7B
This fine-tuned variant of the NextCoder 7B 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.
NextCoder Mirage SOL 7B is in active development with additional fine-tuning sessions, & benchmark statistics coming soon!
Provided Quants
| Link | Type | Size/GB | Notes |
|---|---|---|---|
| GGUF | Q4_K_S | 2.3 | fast, recommended |
| GGUF | Q5_K_S | 2.8 | fast, recommended |
| 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: 9 hours & 56 minutes
- GPU: NVIDIA GeForce RTX 3090
- Batches: 500
- Context-Size: 2043
- Batch-size: 1
- Learning-rate: 2e-5
- Training-loss: 1.09
- Eval-loss: 0.89
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.
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Model tree for Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF
Base model
Qwen/Qwen2.5-7B
Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF:# Run inference directly in the terminal: llama-cli -hf Bifrost-AI/NextCoder-Mirage-sol-7B-GGUF: