Instructions to use ToTo-40417/EXLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ToTo-40417/EXLLM with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf ToTo-40417/EXLLM:F16 # Run inference directly in the terminal: llama cli -hf ToTo-40417/EXLLM:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ToTo-40417/EXLLM:F16 # Run inference directly in the terminal: llama cli -hf ToTo-40417/EXLLM: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 ToTo-40417/EXLLM:F16 # Run inference directly in the terminal: ./llama-cli -hf ToTo-40417/EXLLM: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 ToTo-40417/EXLLM:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf ToTo-40417/EXLLM:F16
Use Docker
docker model run hf.co/ToTo-40417/EXLLM:F16
- LM Studio
- Jan
- vLLM
How to use ToTo-40417/EXLLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ToTo-40417/EXLLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ToTo-40417/EXLLM", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ToTo-40417/EXLLM:F16
- Ollama
How to use ToTo-40417/EXLLM with Ollama:
ollama run hf.co/ToTo-40417/EXLLM:F16
- Unsloth Desktop
- Docker Model Runner
How to use ToTo-40417/EXLLM with Docker Model Runner:
docker model run hf.co/ToTo-40417/EXLLM:F16
- Lemonade
How to use ToTo-40417/EXLLM with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ToTo-40417/EXLLM:F16
Run and chat with the model
lemonade run user.EXLLM-F16
List all available models
lemonade list
- Atomic Chat
Download EXLLM8_FORMAT.md from ToTo-40417/EXLLM: direct link, hf CLI and curl.
- Browser
- Download file 1.29 kB
-
https://huggingface.co/ToTo-40417/EXLLM/resolve/main/EXLLM8_FORMAT.md
- Command line
-
hf download hf://ToTo-40417/EXLLM/EXLLM8_FORMAT.md
-
curl -L -o EXLLM8_FORMAT.md https://huggingface.co/ToTo-40417/EXLLM/resolve/main/EXLLM8_FORMAT.md
EXLLM8 binary format v1
All integer fields are little-endian.
File header
| Field | Type | Value |
|---|---|---|
| magic | 8 bytes | EXLLM8\0\0 |
| format_version | uint32 | 1 |
| tensor_count | uint32 | number of serialized tensors |
Tensor record
Each tensor is stored as:
name_len: uint16ndim: uint8qtype: uint8name:name_lenUTF-8 bytesdims:ndim× uint32scale_count: uint32data_bytes: uint32scales:scale_count× float32data:data_bytesbytes
qtype 1 — row-wise int8
Used for 2D matrices. If a weight is shaped [rows, cols], one float32 symmetric scale is stored per row and the matrix data is signed int8.
Approximate reconstruction:
weight[row, col] = int8_value[row, col] * scale[row]
Quantized values are limited to [-127, 127].
qtype 2 — float16
Used for the small RMSNorm vectors. scale_count is zero and data is IEEE-754 binary16.
Weight tying
lm_head.weight is not stored. It aliases tok.weight. The companion manifest declares this alias explicitly.
Deployment note
The float scales are an interchange representation. A SH4/no-FPU deployment should translate them into the fixed-point scale representation chosen by the C inference kernel.