Instructions to use iSolver-AI/FEnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use iSolver-AI/FEnet with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="iSolver-AI/FEnet", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("iSolver-AI/FEnet", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("iSolver-AI/FEnet", trust_remote_code=True)

llama-cpp-python

How to use iSolver-AI/FEnet with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="iSolver-AI/FEnet",
	filename="qwen2.5-0.5b-instruct-f16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use iSolver-AI/FEnet with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf iSolver-AI/FEnet:F16
# Run inference directly in the terminal:
llama-cli -hf iSolver-AI/FEnet:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf iSolver-AI/FEnet:F16
# Run inference directly in the terminal:
llama-cli -hf iSolver-AI/FEnet:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf iSolver-AI/FEnet:F16
# Run inference directly in the terminal:
./llama-cli -hf iSolver-AI/FEnet:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf iSolver-AI/FEnet:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf iSolver-AI/FEnet:F16

Use Docker

docker model run hf.co/iSolver-AI/FEnet:F16

LM Studio
Jan

vLLM

How to use iSolver-AI/FEnet with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "iSolver-AI/FEnet"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "iSolver-AI/FEnet",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/iSolver-AI/FEnet:F16

SGLang

How to use iSolver-AI/FEnet 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 "iSolver-AI/FEnet" \
    --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": "iSolver-AI/FEnet",
		"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 "iSolver-AI/FEnet" \
        --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": "iSolver-AI/FEnet",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use iSolver-AI/FEnet with Ollama:
```
ollama run hf.co/iSolver-AI/FEnet:F16
```

Unsloth Studio new

How to use iSolver-AI/FEnet with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for iSolver-AI/FEnet to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for iSolver-AI/FEnet to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for iSolver-AI/FEnet to start chatting

Pi new

How to use iSolver-AI/FEnet with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf iSolver-AI/FEnet:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "FEnet"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use iSolver-AI/FEnet with Docker Model Runner:
```
docker model run hf.co/iSolver-AI/FEnet:F16
```

Lemonade

How to use iSolver-AI/FEnet with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull iSolver-AI/FEnet:F16

Run and chat with the model

lemonade run user.FEnet-F16

List all available models

lemonade list

test webhook

Paper:

✅来源于HF+arxiv，完整输入HF链接的论文：F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching
✅来源于HF+arxiv，完整输入arxiv链接的论文：https://arxiv.org/abs/2410.11817
来源于HF+arxiv，仅输入标题+编号的论文：Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction
来源于HF+arxiv，仅输入标题的论文：Exploring Model Kinship for Merging Large Language Models
✅来源于HF+arxiv，输入「arxiv:编号」的论文：arxiv:2410.12381
✅来源于HF+arxiv，输入链接不带https://前缀：arxiv.org/abs/2410.09401
✅仅来源于arxiv，完整输入arxiv链接的论文：Improving Prototypical Parts Abstraction for Case-Based Reasoning Explanations Designed for the Kidney Stone Type Recognition，因为有READme引用而自动导入该paper到Daily Paper,变成arxiv和HF都有的论文
✅仅来源于arxiv，完整输入arxiv链接的论文：Aharonov-Bohm effects on the GUP framework，会因为有READme引用而自动导入该paper到Daily Paper
仅来源于arxiv，仅输入编号的论文：2409.00821
仅来源于arxiv，仅输入标题的论文：An Augmentation-based Model Re-adaptation Framework for Robust Image Segmentation
非arxiv论文： @inproceedings{DBLP:conf/nips/XuLCLQ21, author={Yong Xu and Feng Li and Zhile Chen and Jinxiu Liang and Yuhui Quan}, title={Encoding Spatial Distribution of Convolutional Features for Texture Representation}, year={2021}, cdate={1609459200000}, pages={22732-22744}, url={https://proceedings.neurips.cc/paper/2021/hash/c04c19c2c2474dbf5f7ac4372c5b9af1-Abstract.html}, booktitle={NeurIPS}, crossref={conf/nips/2021} }