Instructions to use tdolega/rag-tge_pl_Llama-3-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tdolega/rag-tge_pl_Llama-3-8B with Transformers:

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

pipe = pipeline("text-generation", model="tdolega/rag-tge_pl_Llama-3-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tdolega/rag-tge_pl_Llama-3-8B")
model = AutoModelForCausalLM.from_pretrained("tdolega/rag-tge_pl_Llama-3-8B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use tdolega/rag-tge_pl_Llama-3-8B 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 tdolega/rag-tge_pl_Llama-3-8B:Q8_0
# Run inference directly in the terminal:
llama cli -hf tdolega/rag-tge_pl_Llama-3-8B:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tdolega/rag-tge_pl_Llama-3-8B:Q8_0
# Run inference directly in the terminal:
llama cli -hf tdolega/rag-tge_pl_Llama-3-8B:Q8_0

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf tdolega/rag-tge_pl_Llama-3-8B:Q8_0
# Run inference directly in the terminal:
./build/bin/llama cli -hf tdolega/rag-tge_pl_Llama-3-8B:Q8_0

Use Docker

docker model run hf.co/tdolega/rag-tge_pl_Llama-3-8B:Q8_0

Llama new
LM Studio
Jan

vLLM

How to use tdolega/rag-tge_pl_Llama-3-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tdolega/rag-tge_pl_Llama-3-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tdolega/rag-tge_pl_Llama-3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tdolega/rag-tge_pl_Llama-3-8B:Q8_0

SGLang

How to use tdolega/rag-tge_pl_Llama-3-8B 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 "tdolega/rag-tge_pl_Llama-3-8B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tdolega/rag-tge_pl_Llama-3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "tdolega/rag-tge_pl_Llama-3-8B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tdolega/rag-tge_pl_Llama-3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use tdolega/rag-tge_pl_Llama-3-8B with Ollama:
```
ollama run hf.co/tdolega/rag-tge_pl_Llama-3-8B:Q8_0
```
Unsloth Desktop
Docker Model Runner
How to use tdolega/rag-tge_pl_Llama-3-8B with Docker Model Runner:
```
docker model run hf.co/tdolega/rag-tge_pl_Llama-3-8B:Q8_0
```

Lemonade

How to use tdolega/rag-tge_pl_Llama-3-8B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tdolega/rag-tge_pl_Llama-3-8B:Q8_0

Run and chat with the model

lemonade run user.rag-tge_pl_Llama-3-8B-Q8_0

List all available models

lemonade list

Atomic Chat

Meta-Llama-3-8B-Instruct finetuned on rag-tge_finetuning-dataset_pl.for rag-tge project. Available as safetensors BF16 and GGUF Q8.

Downloads last month: 42

Safetensors

Model size

8B params

Tensor type

BF16

tdolega
/

rag-tge_pl_Llama-3-8B

Dataset used to train tdolega/rag-tge_pl_Llama-3-8B