Instructions to use anilguven/Llama3.1-8b-TrSummarization-unsloth-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use anilguven/Llama3.1-8b-TrSummarization-unsloth-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="anilguven/Llama3.1-8b-TrSummarization-unsloth-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("anilguven/Llama3.1-8b-TrSummarization-unsloth-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use anilguven/Llama3.1-8b-TrSummarization-unsloth-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 anilguven/Llama3.1-8b-TrSummarization-unsloth-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf anilguven/Llama3.1-8b-TrSummarization-unsloth-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf anilguven/Llama3.1-8b-TrSummarization-unsloth-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf anilguven/Llama3.1-8b-TrSummarization-unsloth-GGUF:Q8_0

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 anilguven/Llama3.1-8b-TrSummarization-unsloth-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf anilguven/Llama3.1-8b-TrSummarization-unsloth-GGUF:Q8_0

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 anilguven/Llama3.1-8b-TrSummarization-unsloth-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf anilguven/Llama3.1-8b-TrSummarization-unsloth-GGUF:Q8_0

Use Docker

docker model run hf.co/anilguven/Llama3.1-8b-TrSummarization-unsloth-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use anilguven/Llama3.1-8b-TrSummarization-unsloth-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "anilguven/Llama3.1-8b-TrSummarization-unsloth-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "anilguven/Llama3.1-8b-TrSummarization-unsloth-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/anilguven/Llama3.1-8b-TrSummarization-unsloth-GGUF:Q8_0

SGLang

How to use anilguven/Llama3.1-8b-TrSummarization-unsloth-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "anilguven/Llama3.1-8b-TrSummarization-unsloth-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "anilguven/Llama3.1-8b-TrSummarization-unsloth-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "anilguven/Llama3.1-8b-TrSummarization-unsloth-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "anilguven/Llama3.1-8b-TrSummarization-unsloth-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use anilguven/Llama3.1-8b-TrSummarization-unsloth-GGUF with Ollama:
```
ollama run hf.co/anilguven/Llama3.1-8b-TrSummarization-unsloth-GGUF:Q8_0
```
Unsloth Desktop
Docker Model Runner
How to use anilguven/Llama3.1-8b-TrSummarization-unsloth-GGUF with Docker Model Runner:
```
docker model run hf.co/anilguven/Llama3.1-8b-TrSummarization-unsloth-GGUF:Q8_0
```

Lemonade

How to use anilguven/Llama3.1-8b-TrSummarization-unsloth-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull anilguven/Llama3.1-8b-TrSummarization-unsloth-GGUF:Q8_0

Run and chat with the model

lemonade run user.Llama3.1-8b-TrSummarization-unsloth-GGUF-Q8_0

List all available models

lemonade list

Atomic Chat

anilguven commited on Dec 19, 2024

Commit

851d257

verified ·

1 Parent(s): 531ae3a

Update README.md

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Files changed (1) hide show

README.md +7 -2

README.md CHANGED Viewed

@@ -6,9 +6,14 @@ tags:
 - unsloth
 - llama
 - gguf
 license: apache-2.0
 language:
-- en
 ---
 # Uploaded  model
@@ -19,4 +24,4 @@ language:
 This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

 - unsloth
 - llama
 - gguf
+- turkish
+- summarization
 license: apache-2.0
 language:
+- tr
+datasets:
+- musabg/wikipedia-tr-summarization
+pipeline_tag: text-generation
 ---
 # Uploaded  model
 This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)