Instructions to use qwp4w3hyb/Starling-LM-7B-beta-iMat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use qwp4w3hyb/Starling-LM-7B-beta-iMat-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("qwp4w3hyb/Starling-LM-7B-beta-iMat-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use qwp4w3hyb/Starling-LM-7B-beta-iMat-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 qwp4w3hyb/Starling-LM-7B-beta-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf qwp4w3hyb/Starling-LM-7B-beta-iMat-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 qwp4w3hyb/Starling-LM-7B-beta-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf qwp4w3hyb/Starling-LM-7B-beta-iMat-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 qwp4w3hyb/Starling-LM-7B-beta-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf qwp4w3hyb/Starling-LM-7B-beta-iMat-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 qwp4w3hyb/Starling-LM-7B-beta-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf qwp4w3hyb/Starling-LM-7B-beta-iMat-GGUF:Q4_K_M

Use Docker

docker model run hf.co/qwp4w3hyb/Starling-LM-7B-beta-iMat-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use qwp4w3hyb/Starling-LM-7B-beta-iMat-GGUF with Ollama:
```
ollama run hf.co/qwp4w3hyb/Starling-LM-7B-beta-iMat-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use qwp4w3hyb/Starling-LM-7B-beta-iMat-GGUF with Docker Model Runner:
```
docker model run hf.co/qwp4w3hyb/Starling-LM-7B-beta-iMat-GGUF:Q4_K_M
```

Lemonade

How to use qwp4w3hyb/Starling-LM-7B-beta-iMat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull qwp4w3hyb/Starling-LM-7B-beta-iMat-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Starling-LM-7B-beta-iMat-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Starling-LM-7B-beta-iMat-GGUF / README.md

qwp4w3hyb

Update README.md

7ce04f8 verified over 2 years ago

preview code

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metadata

base_model: Nexusflow/Starling-LM-7B-beta
datasets:
  - berkeley-nest/Nectar
language:
  - en
library_name: transformers
tags:
  - starling
  - reward model
  - RLHF
  - RLAIF
model-index:
  - name: Nexusflow/Starling-LM-7B-beta-iMat-GGUF
    results: []
license: apache-2.0

Starling-LM-7B-beta-iMat-GGUF

Source Model: Nexusflow/Starling-LM-7B-beta

Quantized with llama.cpp commit 46acb3676718b983157058aecf729a2064fc7d34

Imatrix was generated from the f16 gguf via this command:

./imatrix -c 512 -m $out_path/$base_quant_name -f $llama_cpp_path/groups_merged.txt -o $out_path/imat-f16-gmerged.dat

Using the dataset from here