Instructions to use Bifrost-AI/NextCoder-Mirage-sol-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Bifrost-AI/NextCoder-Mirage-sol-7B 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")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Bifrost-AI/NextCoder-Mirage-sol-7B")
model = AutoModelForCausalLM.from_pretrained("Bifrost-AI/NextCoder-Mirage-sol-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Bifrost-AI/NextCoder-Mirage-sol-7B 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"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Bifrost-AI/NextCoder-Mirage-sol-7B

SGLang

How to use Bifrost-AI/NextCoder-Mirage-sol-7B 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" \
    --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",
		"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" \
        --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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Bifrost-AI/NextCoder-Mirage-sol-7B with Docker Model Runner:
```
docker model run hf.co/Bifrost-AI/NextCoder-Mirage-sol-7B
```

BifrostTitan commited on Jul 14, 2025

Commit

6a54c0c

verified ·

1 Parent(s): f3d8d70

Update README.md

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@@ -1,3 +1,47 @@
----
-license: mit
----

+---
+license: mit
+datasets:
+- Bifrost-AI/Solana-Vanguard-Challenge
+language:
+- en
+metrics:
+- accuracy
+- code_eval
+base_model:
+- microsoft/NextCoder-7B
+pipeline_tag: text-generation
+tags:
+- code
+- finance
+- chat
+- text-generation
+- large-language-model
+library_name: transformers
+---
+# 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!
+## 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.
+## 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.