Text Generation
Transformers
Safetensors
English
qwen3_moe
text-generation-inference
unsloth
hybrid-thinking
coding-assistant
conversational
Instructions to use Daemontatox/FerrisMind with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Daemontatox/FerrisMind with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Daemontatox/FerrisMind") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Daemontatox/FerrisMind") model = AutoModelForCausalLM.from_pretrained("Daemontatox/FerrisMind", device_map="auto") 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 Daemontatox/FerrisMind with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Daemontatox/FerrisMind" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Daemontatox/FerrisMind", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Daemontatox/FerrisMind
- SGLang
How to use Daemontatox/FerrisMind 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 "Daemontatox/FerrisMind" \ --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": "Daemontatox/FerrisMind", "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 "Daemontatox/FerrisMind" \ --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": "Daemontatox/FerrisMind", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Desktop
- Docker Model Runner
How to use Daemontatox/FerrisMind with Docker Model Runner:
docker model run hf.co/Daemontatox/FerrisMind
metadata
base_model:
- Qwen/Qwen3-Coder-30B-A3B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3_moe
- hybrid-thinking
- coding-assistant
license: apache-2.0
language:
- en
datasets:
- Tesslate/Rust_Dataset
- Tesslate/Gradient-Reasoning
library_name: transformers
new_version: Daemontatox/FerrisMind
FerrisMind (Daemontatox, 2025)
Model Details
- Model name: Daemontatox/FerrisMind
- Developed by: Daemontatox
- Year released: 2025
- License: apache-2.0
- Base model: unsloth/qwen3-coder-30b-a3b-instruct
- Model type: Instruction-tuned large language model for code generation, specifically designed to mimic hybrid thinking and utilize it in coding instruct models.
Model Summary
FerrisMind is a finetuned variant of Qwen3 Coder Flash, specialized for Rust programming. It was trained using GRPO in an attempt to mimic hybrid thinking and utilize it in coding instruct models. It is optimized for:
- Idiomatic Rust generation
- High-performance and memory-safe code practices
- Fast inference and completion speed
- Practical coding assistant tasks, from boilerplate scaffolding to compiler-level optimizations
Intended Use
- Rust development assistance
- Generating idiomatic and production-ready Rust code
- Accelerating prototyping and compiler-level workflows
- Educational use for learning Rust best practices
Out of Scope
- Non-code general conversation
- Unsafe or malicious code generation
Training
- Finetuned from: unsloth/qwen3-coder-30b-a3b-instruct
- Objective: Specialization in Rust code generation and idiomatic best practices, mimicking hybrid thinking.
- Methods: Instruction tuning with GRPO and domain-specific data
Limitations
- May generate non-compiling Rust code in complex cases
Example Usage
// Example: Async file reader in idiomatic Rust
use tokio::fs::File;
use tokio::io::{self, AsyncReadExt};
#[tokio::main]
async fn main() -> io::Result<()> {
let mut file = File::open("example.txt").await?;
let mut contents = String::new();
file.read_to_string(&mut contents).await?;
println!("File content: {}", contents);
Ok(())
}
