Instructions to use lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full with Transformers:

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

pipe = pipeline("text-generation", model="lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full")
model = AutoModelForCausalLM.from_pretrained("lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full

SGLang

How to use lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full 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 "lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full" \
    --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": "lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full",
		"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 "lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full" \
        --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": "lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full with Docker Model Runner:
```
docker model run hf.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full
```

ptrdvn commited on May 30, 2024

Commit

7c59810

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@@ -27,6 +27,14 @@ Note that this model has a non-commerical license as we used the Command R and C
 We are currently working on a developing a commerically usable model, so stay tuned for that!
 # Model results
 We compare the MT-Bench scores across 6 languages for our 4 ORPO trained models, as well as some baselines:

 We are currently working on a developing a commerically usable model, so stay tuned for that!
+# Model list
+We have ORPO trained the following models using different proportions of the [lightblue/mitsu](https://huggingface.co/datasets/lightblue/mitsu) dataset:
+* Trained on the top/bottom responses of all prompts in the dataset: [lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-full)
+* Trained on the top/bottom responses of the prompts of the 75\% most consistently ranked responses in the dataset: [lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top75](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top75)
+* Trained on the top/bottom responses of the prompts of the 50\% most consistently ranked responses in the dataset: [lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half)
+* Trained on the top/bottom responses of the prompts of the 25\% most consistently ranked responses in the dataset: [lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top25](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top25)
 # Model results
 We compare the MT-Bench scores across 6 languages for our 4 ORPO trained models, as well as some baselines: