Text Generation
GGUF
Japanese
japanese
instruction-tuning
little-language-model
tiny-language-model
edge-ai
embedded-ai
ex-word
llama-cpp
lm-studio
custom-code
conversational
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
File size: 1,857 Bytes
80300e5 4145f9d 80300e5 | 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 | import argparse,json,struct,sys
from pathlib import Path
import numpy as np,torch
ROOT=Path(__file__).resolve().parents[1];sys.path.insert(0,str(ROOT))
from src.model import EXLLM,EXLLMConfig
MAGIC=b'EXLLM8\0\0'
def load_bin(path,manifest_path):
man=json.loads(Path(manifest_path).read_text(encoding='utf-8'));m=EXLLM(EXLLMConfig(**man['config']));vals={}
with open(path,'rb') as f:
if f.read(8)!=MAGIC:raise ValueError('bad magic')
ver,n=struct.unpack('<II',f.read(8));
if ver!=1:raise ValueError('unsupported format')
for _ in range(n):
nl,nd,qt=struct.unpack('<HBB',f.read(4));name=f.read(nl).decode();dims=[struct.unpack('<I',f.read(4))[0] for _ in range(nd)];ns,nb=struct.unpack('<II',f.read(8));sc=np.frombuffer(f.read(ns*4),dtype='<f4').copy() if ns else np.array([],dtype=np.float32);raw=f.read(nb)
if qt==1:a=np.frombuffer(raw,dtype=np.int8).reshape(dims).astype(np.float32)*sc[:,None]
elif qt==2:a=np.frombuffer(raw,dtype='<f2').reshape(dims).astype(np.float32)
else:raise ValueError(qt)
vals[name]=torch.from_numpy(a.copy())
sd=m.state_dict();aliases=man.get('aliases',{})
for k in sd:
src=aliases.get(k,k)
if src in vals:sd[k]=vals[src]
m.load_state_dict(sd);m.eval();return m,man
def main():
ap=argparse.ArgumentParser();ap.add_argument('--bin',default='weights/EXLLM-v1.1-5m-int8.bin');ap.add_argument('--manifest',default='weights/EXLLM-v1.1-5m-int8.manifest.json');ap.add_argument('--out',default='weights/EXLLM-v1.1-5m-int8-dequant-test.pt');a=ap.parse_args();m,man=load_bin(ROOT/a.bin,ROOT/a.manifest);torch.save({'model':m.state_dict(),'config':m.cfg.__dict__,'meta':{'name':'EXLLM','developer':'ToTo','version':'1.1.0-5m','quantized_source':a.bin}},ROOT/a.out);print(a.out)
if __name__=='__main__':main()
|