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Model: Bifrost-AI/Qwen3-Bifrost-SOL-4B-GGUF
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---
license: mit
datasets:
- Bifrost-AI/Solana-Vanguard-Challenge
language:
- en
metrics:
- accuracy
- code_eval
base_model:
- Bifrost-AI/Qwen3-Bifrost-SOL-4B
pipeline_tag: text-generation
tags:
- code
- finance
- chat
- text-generation
- large-language-model
library_name: transformers
---
# 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](https://huggingface.co/Bifrost-AI/Qwen3-Bifrost-SOL-4B-gguf/resolve/main/Qwen3-Bifrost-SOL-4B.IQ1_S.gguf) | IQ1_S | 1.1 | very low quality |
| [GGUF](https://huggingface.co/Bifrost-AI/Qwen3-Bifrost-SOL-4B-gguf/resolve/main/Qwen3-Bifrost-SOL-4B.IQ1_M.gguf) | IQ1_M | 1.2 | very low quality |
| [GGUF](https://huggingface.co/Bifrost-AI/Qwen3-Bifrost-SOL-4B-gguf/resolve/main/Qwen3-Bifrost-SOL-4B.TQ1_0.gguf) | TQ1_0 | 1.2 | very low quality |
| [GGUF](https://huggingface.co/Bifrost-AI/Qwen3-Bifrost-SOL-4B-gguf/resolve/main/Qwen3-Bifrost-SOL-4B.IQ2_S.gguf) | IQ2_S | 1.4 | fast, lower quality |
| [GGUF](https://huggingface.co/Bifrost-AI/Qwen3-Bifrost-SOL-4B-gguf/resolve/main/Qwen3-Bifrost-SOL-4B.Q2_K.gguf) | Q2_K | 1.6 | fast, lower quality |
| [GGUF](https://huggingface.co/Bifrost-AI/Qwen3-Bifrost-SOL-4B-gguf/resolve/main/Qwen3-Bifrost-SOL-4B.Q4_K_M.gguf) | Q4_K_M | 2.5 | fast, recommended |
| [GGUF](https://huggingface.co/Bifrost-AI/Qwen3-Bifrost-SOL-4B-gguf/resolve/main/Qwen3-Bifrost-SOL-4B.Q4_K_S.gguf) | Q4_K_S | 2.3 | fast, recommended |
| [GGUF](https://huggingface.co/Bifrost-AI/Qwen3-Bifrost-SOL-4B-gguf/resolve/main/Qwen3-Bifrost-SOL-4B.Q4_0.gguf) | Q4_0 | 2.3 | fast, recommended |
| [GGUF](https://huggingface.co/Bifrost-AI/Qwen3-Bifrost-SOL-4B-gguf/resolve/main/Qwen3-Bifrost-SOL-4B.Q5_K_S.gguf) | Q5_K_S | 2.7 | |
| [GGUF](https://huggingface.co/Bifrost-AI/Qwen3-Bifrost-SOL-4B-gguf/resolve/main/Qwen3-Bifrost-SOL-4B.Q5_K_M.gguf) | Q5_K_M | 2.8 | |
| [GGUF](https://huggingface.co/Bifrost-AI/Qwen3-Bifrost-SOL-4B-gguf/resolve/main/Qwen3-Bifrost-SOL-4B.Q6_K.gguf) | Q6_K | 3.1 | very good quality |
| [GGUF](https://huggingface.co/Bifrost-AI/Qwen3-Bifrost-SOL-4B-gguf/resolve/main/Qwen3-Bifrost-SOL-4B.Q8_0.gguf) | Q8_0 | 4.0 | fast, best quality |
| [GGUF](https://huggingface.co/Bifrost-AI/Qwen3-Bifrost-SOL-4B-gguf/resolve/main/Qwen3-Bifrost-SOL-4B.f16.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.