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)# pip install -U transformers accelerate # 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=256) 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
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Download README.md from Daemontatox/FerrisMind: direct link, hf CLI and curl.
- Browser
- Download file 2.3 kB
-
https://huggingface.co/Daemontatox/FerrisMind/resolve/main/README.md
- Command line
-
hf download hf://Daemontatox/FerrisMind/README.md
-
curl -L -o README.md https://huggingface.co/Daemontatox/FerrisMind/resolve/main/README.md
2.3 kB
| 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(()) | |
| } |