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
File size: 2,302 Bytes
56c376e 512e6e4 56c376e af2f80a 56c376e 63cb732 3249268 56c376e b04510d 56c376e 63cb732 56c376e 63cb732 af2f80a 56c376e 63cb732 af2f80a 63cb732 56c376e 63cb732 af2f80a 63cb732 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 | ---
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](https://huggingface.co/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
```rust
// 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(())
} |