Instructions to use alexithymi1a/comma-qlora-it-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use alexithymi1a/comma-qlora-it-gguf 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 alexithymi1a/comma-qlora-it-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf alexithymi1a/comma-qlora-it-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf alexithymi1a/comma-qlora-it-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf alexithymi1a/comma-qlora-it-gguf: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 alexithymi1a/comma-qlora-it-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf alexithymi1a/comma-qlora-it-gguf: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 alexithymi1a/comma-qlora-it-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf alexithymi1a/comma-qlora-it-gguf:Q4_K_M

Use Docker

docker model run hf.co/alexithymi1a/comma-qlora-it-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use alexithymi1a/comma-qlora-it-gguf with Ollama:
```
ollama run hf.co/alexithymi1a/comma-qlora-it-gguf:Q4_K_M
```

Unsloth Studio

How to use alexithymi1a/comma-qlora-it-gguf 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 alexithymi1a/comma-qlora-it-gguf 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 alexithymi1a/comma-qlora-it-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for alexithymi1a/comma-qlora-it-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use alexithymi1a/comma-qlora-it-gguf with Docker Model Runner:
```
docker model run hf.co/alexithymi1a/comma-qlora-it-gguf:Q4_K_M
```

Lemonade

How to use alexithymi1a/comma-qlora-it-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull alexithymi1a/comma-qlora-it-gguf:Q4_K_M

Run and chat with the model

lemonade run user.comma-qlora-it-gguf-Q4_K_M

List all available models

lemonade list

Model Card for Comma-QLoRa-IT

QLora instruction fine-tuning on Common-Pile's Comma-v0.1-2T model. The goal is to make a usable assistant while maintaining the goal of not using stolen data.

dianab1729/Ada-1 (which was already SFT'd on no-robots) was used as a base to skip a step.

SFT on The human authored, english part from CohereLabs/aya_dataset
SFT on databricks/databricks-dolly-15k
DPO on OpenAssistant/oasst1

All QLoRA. It ain't much, but it's honest work. :)

Planning on making a much better version of this, I'm just learning on this rn.

Downloads last month: 77

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

4-bit

8-bit

16-bit

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Model tree for alexithymi1a/comma-qlora-it-gguf

Base model

common-pile/comma-v0.1-2t

Quantized

(5)

this model

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comma-qlora-it-gguf

Model Card for Comma-QLoRa-IT

Model tree for alexithymi1a/comma-qlora-it-gguf

Datasets used to train alexithymi1a/comma-qlora-it-gguf