legacy-datasets/wikipedia
Updated • 151k • 661
How to use afrideva/llama-160m-GGUF with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf afrideva/llama-160m-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf afrideva/llama-160m-GGUF:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf afrideva/llama-160m-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf afrideva/llama-160m-GGUF:Q4_K_M
# 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 afrideva/llama-160m-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf afrideva/llama-160m-GGUF:Q4_K_M
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 afrideva/llama-160m-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf afrideva/llama-160m-GGUF:Q4_K_M
docker model run hf.co/afrideva/llama-160m-GGUF:Q4_K_M
How to use afrideva/llama-160m-GGUF with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "afrideva/llama-160m-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "afrideva/llama-160m-GGUF",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/afrideva/llama-160m-GGUF:Q4_K_M
How to use afrideva/llama-160m-GGUF with Ollama:
ollama run hf.co/afrideva/llama-160m-GGUF:Q4_K_M
How to use afrideva/llama-160m-GGUF with Unsloth Studio:
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 afrideva/llama-160m-GGUF to start chatting
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 afrideva/llama-160m-GGUF to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for afrideva/llama-160m-GGUF to start chatting
How to use afrideva/llama-160m-GGUF with Docker Model Runner:
docker model run hf.co/afrideva/llama-160m-GGUF:Q4_K_M
How to use afrideva/llama-160m-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull afrideva/llama-160m-GGUF:Q4_K_M
lemonade run user.llama-160m-GGUF-Q4_K_M
lemonade list
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf afrideva/llama-160m-GGUF:# Run inference directly in the terminal:
llama cli -hf afrideva/llama-160m-GGUF:# 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 afrideva/llama-160m-GGUF:# Run inference directly in the terminal:
./llama-cli -hf afrideva/llama-160m-GGUF: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 afrideva/llama-160m-GGUF:# Run inference directly in the terminal:
./build/bin/llama-cli -hf afrideva/llama-160m-GGUF:docker model run hf.co/afrideva/llama-160m-GGUF:Quantized GGUF model files for llama-160m from JackFram
| Name | Quant method | Size |
|---|---|---|
| llama-160m.fp16.gguf | fp16 | 326.58 MB |
| llama-160m.q2_k.gguf | q2_k | 77.23 MB |
| llama-160m.q3_k_m.gguf | q3_k_m | 87.54 MB |
| llama-160m.q4_k_m.gguf | q4_k_m | 104.03 MB |
| llama-160m.q5_k_m.gguf | q5_k_m | 119.04 MB |
| llama-160m.q6_k.gguf | q6_k | 135.00 MB |
| llama-160m.q8_0.gguf | q8_0 | 174.33 MB |
This is a LLaMA-like model with only 160M parameters trained on Wikipedia and part of the C4-en and C4-realnewslike datasets.
No evaluation has been conducted yet, so use it with care.
The model is mainly developed as a base Small Speculative Model in the SpecInfer paper.
To cite the model, please use
@misc{miao2023specinfer,
title={SpecInfer: Accelerating Generative LLM Serving with Speculative Inference and Token Tree Verification},
author={Xupeng Miao and Gabriele Oliaro and Zhihao Zhang and Xinhao Cheng and Zeyu Wang and Rae Ying Yee Wong and Zhuoming Chen and Daiyaan Arfeen and Reyna Abhyankar and Zhihao Jia},
year={2023},
eprint={2305.09781},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Base model
JackFram/llama-160m
Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf afrideva/llama-160m-GGUF:# Run inference directly in the terminal: llama cli -hf afrideva/llama-160m-GGUF: