Instructions to use lukestanley/ChillTranslator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use lukestanley/ChillTranslator 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 lukestanley/ChillTranslator:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf lukestanley/ChillTranslator:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lukestanley/ChillTranslator:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf lukestanley/ChillTranslator:Q4_K_M

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 lukestanley/ChillTranslator:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf lukestanley/ChillTranslator:Q4_K_M

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 lukestanley/ChillTranslator:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lukestanley/ChillTranslator:Q4_K_M

Use Docker

docker model run hf.co/lukestanley/ChillTranslator:Q4_K_M

LM Studio
Jan

vLLM

How to use lukestanley/ChillTranslator with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lukestanley/ChillTranslator"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lukestanley/ChillTranslator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/lukestanley/ChillTranslator:Q4_K_M

Ollama
How to use lukestanley/ChillTranslator with Ollama:
```
ollama run hf.co/lukestanley/ChillTranslator:Q4_K_M
```

Unsloth Studio

How to use lukestanley/ChillTranslator 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 lukestanley/ChillTranslator 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 lukestanley/ChillTranslator to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lukestanley/ChillTranslator to start chatting

Docker Model Runner
How to use lukestanley/ChillTranslator with Docker Model Runner:
```
docker model run hf.co/lukestanley/ChillTranslator:Q4_K_M
```

Lemonade

How to use lukestanley/ChillTranslator with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lukestanley/ChillTranslator:Q4_K_M

Run and chat with the model

lemonade run user.ChillTranslator-Q4_K_M

List all available models

lemonade list

Atomic Chat

❄️ ChillTranslator 🤬 ➡️ 😎💬

Overview

ChillTranslator uses Microsoft's Phi 2 as the base model. It's been fine-tuned on a dataset made up of calm versions of internet comments. These comments are meant to be output as JSON with a grammar specified.

Intent

The project is an experiment in how we can use AI to tone down heated online comments that are worth discussing, steering clear of pure hate speech (not much can be done for that, I suppose). It's an exploration into creating tools that could help make online discussions more constructive.

Model Details

Base Model: Microsoft Phi 2, chosen for its efficiency and capability in language understanding and generation.
Fine-tuning: Performed on a curated dataset designed to encourage more respectful and thoughtful online interactions.
File Info: The model file ChillTranslator_Q4_K_M.gguf is under 2 GB and works with llama.cpp. It’s meant to run with a grammar file, producing JSON objects to ensure it generates only the requested output.

Example Usage

This is an example of how to run ChillTranslator with the necessary options:

llama.cpp/main -m ChillTranslator_Q4_K_M.gguf --interactive-first --grammar-file ChillTranslator.grammar

And here's a snippet of a llama.cpp grammar` file that makes it produce more predictable output:

root ::= TextRevision
BetterTerm ::= "{" ws "\"old\":" ws string "," ws "\"new\":" ws stringlist "}"
BetterTermlist ::= "[]" | "[" ws BetterTerm ("," ws BetterTerm)* "]"
TextRevision ::= "{" ws "\"better_terms\":" ws BetterTermlist "," ws "\"minimal_fix\":" ws string "," ws "\"nvc_perspective\":" ws string "," ws "\"constructive\":" ws string "," ws "\"hybrid\":" ws string "," ws "\"final\":" ws string ws "}"
TextRevisionlist ::= "[]" | "[" ws TextRevision ("," ws TextRevision)* "]"
string ::= "\"" ([^"]*) "\""
boolean ::= "true" | "false"
ws ::= [ \t\n]*
number ::= [0-9]+ "."? [0-9]*
stringlist ::= "[" ws "]" | "[" ws string ("," ws string)* ws "]"
numberlist ::= "[" ws "]" | "[" ws number ("," ws number)* ws "]"

Downloads last month: 24

GGUF

Model size

3B params

Architecture

phi2

Hardware compatibility

4-bit

Model tree for lukestanley/ChillTranslator

Base model

microsoft/phi-2

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