Instructions to use lwef/llm-bench-upload-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lwef/llm-bench-upload-1 with Transformers:

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

pipe = pipeline("text-generation", model="lwef/llm-bench-upload-1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("lwef/llm-bench-upload-1")
model = AutoModelForMultimodalLM.from_pretrained("lwef/llm-bench-upload-1")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lwef/llm-bench-upload-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lwef/llm-bench-upload-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lwef/llm-bench-upload-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lwef/llm-bench-upload-1

SGLang

How to use lwef/llm-bench-upload-1 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 "lwef/llm-bench-upload-1" \
    --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": "lwef/llm-bench-upload-1",
		"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 "lwef/llm-bench-upload-1" \
        --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": "lwef/llm-bench-upload-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use lwef/llm-bench-upload-1 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 lwef/llm-bench-upload-1 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 lwef/llm-bench-upload-1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lwef/llm-bench-upload-1 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="lwef/llm-bench-upload-1",
    max_seq_length=2048,
)

Docker Model Runner
How to use lwef/llm-bench-upload-1 with Docker Model Runner:
```
docker model run hf.co/lwef/llm-bench-upload-1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Developed by: lwef
License: apache-2.0
Finetuned from model : beomi/Llama-3-Open-Ko-8B

korean dialogue summary fine-tuned model

how to use

prompt_template = '''
아래 대화를 요약해 주세요. 대화 형식은 '#대화 참여자#: 대화 내용'입니다.
### 대화 >>>{dialogue}

### 요약 >>>'''

if True:
    from unsloth import FastLanguageModel
    model, tokenizer = FastLanguageModel.from_pretrained(
        model_name = "lwef/llm-bench-upload-1", # YOUR MODEL YOU USED FOR TRAINING
        max_seq_length = 2048,
        dtype = None,
        load_in_4bit = True,
    )
    FastLanguageModel.for_inference(model) # Enable native 2x faster inference
dialogue = '''#P01#: 아 행삶 과제 너무 어려워... 5쪽 쓸게 없는데 ㅡㅡ #P02#: 몬냐몬냐너가더잘써 ㅎㅎ #P01#: 5쪽 대충 의식의 흐름대로 쭉 써야지..이제 1쪽씀 ;; 5쪽 에는 네줄만 적어야지 #P02#: 안대... 뭔가분량중요할거같아 거의꽉채워서쓰셈 #P01#: 못써 쓸말업써 #P02#: 이거중간대체여?? #P01#: ㄴㄴ 그냥 과제임 그래서 더 짜증남'''

formatted_prompt = prompt_template.format(dialogue=dialogue)

# 토크나이징
inputs = tokenizer(
    formatted_prompt,
    return_tensors="pt"
).to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens = 128,
    eos_token_id=tokenizer.eos_token_id, # EOS 토큰을 사용하여 명시적으로 출력의 끝을 지정.
    use_cache = True
)
decoded_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
result = decoded_outputs[0]

print(result)
result = result.split('### 요약 >>>')[-1].strip()
print(result)

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

I highly recommend checking the Unsloth notebook.

Downloads last month: 4

Model tree for lwef/llm-bench-upload-1

Base model

beomi/Llama-3-Open-Ko-8B

Finetuned

(20)

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Quantizations

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