Instructions to use sayhan/Trendyol-LLM-7b-base-v0.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sayhan/Trendyol-LLM-7b-base-v0.1-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="sayhan/Trendyol-LLM-7b-base-v0.1-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("sayhan/Trendyol-LLM-7b-base-v0.1-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use sayhan/Trendyol-LLM-7b-base-v0.1-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 sayhan/Trendyol-LLM-7b-base-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sayhan/Trendyol-LLM-7b-base-v0.1-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 sayhan/Trendyol-LLM-7b-base-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sayhan/Trendyol-LLM-7b-base-v0.1-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 sayhan/Trendyol-LLM-7b-base-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sayhan/Trendyol-LLM-7b-base-v0.1-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 sayhan/Trendyol-LLM-7b-base-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sayhan/Trendyol-LLM-7b-base-v0.1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/sayhan/Trendyol-LLM-7b-base-v0.1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use sayhan/Trendyol-LLM-7b-base-v0.1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sayhan/Trendyol-LLM-7b-base-v0.1-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sayhan/Trendyol-LLM-7b-base-v0.1-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/sayhan/Trendyol-LLM-7b-base-v0.1-GGUF:Q4_K_M

SGLang

How to use sayhan/Trendyol-LLM-7b-base-v0.1-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 "sayhan/Trendyol-LLM-7b-base-v0.1-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": "sayhan/Trendyol-LLM-7b-base-v0.1-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 "sayhan/Trendyol-LLM-7b-base-v0.1-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": "sayhan/Trendyol-LLM-7b-base-v0.1-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use sayhan/Trendyol-LLM-7b-base-v0.1-GGUF with Ollama:
```
ollama run hf.co/sayhan/Trendyol-LLM-7b-base-v0.1-GGUF:Q4_K_M
```

Unsloth Studio

How to use sayhan/Trendyol-LLM-7b-base-v0.1-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 sayhan/Trendyol-LLM-7b-base-v0.1-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 sayhan/Trendyol-LLM-7b-base-v0.1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sayhan/Trendyol-LLM-7b-base-v0.1-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use sayhan/Trendyol-LLM-7b-base-v0.1-GGUF with Docker Model Runner:
```
docker model run hf.co/sayhan/Trendyol-LLM-7b-base-v0.1-GGUF:Q4_K_M
```

Lemonade

How to use sayhan/Trendyol-LLM-7b-base-v0.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sayhan/Trendyol-LLM-7b-base-v0.1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Trendyol-LLM-7b-base-v0.1-GGUF-Q4_K_M

List all available models

lemonade list

sayhan commited on Feb 16, 2024

Commit

f4f2991

verified ·

1 Parent(s): a4bb9e6

Update README.md

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README.md +16 -1

README.md CHANGED Viewed

@@ -18,4 +18,19 @@ alt="drawing" width="400"/>
 <!-- description start -->
 ## Description
 This repo contains GGUF format model files for [Trendyol's Trendyol LLM 7b base v0.1](https://huggingface.co/Trendyol/Trendyol-LLM-7b-base-v0.1)
-<!-- description end -->

 <!-- description start -->
 ## Description
 This repo contains GGUF format model files for [Trendyol's Trendyol LLM 7b base v0.1](https://huggingface.co/Trendyol/Trendyol-LLM-7b-base-v0.1)
+<!-- description end -->
+# Quantization methods
+| quantization method | bits | size     | use case                                            | recommended |
+|---------------------|------|----------|-----------------------------------------------------|-------------|
+| Q2_K                | 2    | 2.59 GB  | smallest, significant quality loss - not recommended for most purposes | ❌         |
+| Q3_K_S              | 3    | 3.01 GB  | very small, high quality loss                       | ❌         |
+| Q3_K_M              | 3    | 3.36 GB  | very small, high quality loss                       | ❌         |
+| Q3_K_L              | 3    | 3.66 GB  | small, substantial quality loss                     | ❌         |
+| Q4_0                | 4    | 3.9 GB  | legacy; small, very high quality loss - prefer using Q3_K_M | ❌         |
+| Q4_K_M              | 4    | 4.15 GB  | medium, balanced quality - recommended              | ✅         |
+| Q5_0                | 5    | 4.73 GB  | legacy; medium, balanced quality - prefer using Q4_K_M | ❌         |
+| Q5_K_S              | 5    | 4.73 GB  | large, low quality loss - recommended               | ✅         |
+| Q5_K_M              | 5    | 4.86 GB  | large, very low quality loss - recommended          | ✅         |
+| Q6_K                | 6    | 5.61 GB  | very large, extremely low quality loss              | ❌         |
+| Q8_0                | 8    | 13.7 GB  | very large, extremely low quality loss - not recommended | ❌         |