Instructions to use compilade/quant-tests with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use compilade/quant-tests with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="compilade/quant-tests",
	filename="TriLM_1.5B_Unpacked-TQ1_0-F16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use compilade/quant-tests with llama.cpp:

Install from brew

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

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf compilade/quant-tests:F16
# Run inference directly in the terminal:
llama-cli -hf compilade/quant-tests: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 compilade/quant-tests:F16
# Run inference directly in the terminal:
./llama-cli -hf compilade/quant-tests: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 compilade/quant-tests:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf compilade/quant-tests:F16

Use Docker

docker model run hf.co/compilade/quant-tests:F16

LM Studio
Jan
Ollama
How to use compilade/quant-tests with Ollama:
```
ollama run hf.co/compilade/quant-tests:F16
```

Unsloth Studio new

How to use compilade/quant-tests 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 compilade/quant-tests 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 compilade/quant-tests to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for compilade/quant-tests to start chatting

Docker Model Runner
How to use compilade/quant-tests with Docker Model Runner:
```
docker model run hf.co/compilade/quant-tests:F16
```

Lemonade

How to use compilade/quant-tests with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull compilade/quant-tests:F16

Run and chat with the model

lemonade run user.quant-tests-F16

List all available models

lemonade list

compilade commited on Dec 12, 2024

Commit

85a696e

1 Parent(s): bc00f19

Add Q4_K_M to the types in the benchmarking script

Browse files

Files changed (1) hide show

bench-TriLMs.sh +2 -2

bench-TriLMs.sh CHANGED Viewed

@@ -6,8 +6,8 @@ cd "$(dirname "$0")"
 MODEL_DIR="bench-TriLMs-models"
 LLAMA_CPP_PATH="."
 sizes=("1.5" "2.4" "3.9")
-types=("TQ1_0" "TQ2_0" "Q8_0" "F16" "BF16")
-gputypes=("Q8_0" "F16" "BF16")
 function gather_models() {
   echo Gather the models

 MODEL_DIR="bench-TriLMs-models"
 LLAMA_CPP_PATH="."
 sizes=("1.5" "2.4" "3.9")
+types=("TQ1_0" "TQ2_0" "Q4_K_M" "Q8_0" "F16" "BF16")
+gputypes=("Q4_K_M" "Q8_0" "F16" "BF16")
 function gather_models() {
   echo Gather the models