Instructions to use Salesforce/LLaMA-3-8B-SFR-Iterative-DPO-R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Salesforce/LLaMA-3-8B-SFR-Iterative-DPO-R with Transformers:

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

pipe = pipeline("text-generation", model="Salesforce/LLaMA-3-8B-SFR-Iterative-DPO-R")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Salesforce/LLaMA-3-8B-SFR-Iterative-DPO-R")
model = AutoModelForCausalLM.from_pretrained("Salesforce/LLaMA-3-8B-SFR-Iterative-DPO-R")
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

vLLM

How to use Salesforce/LLaMA-3-8B-SFR-Iterative-DPO-R with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Salesforce/LLaMA-3-8B-SFR-Iterative-DPO-R

SGLang

How to use Salesforce/LLaMA-3-8B-SFR-Iterative-DPO-R 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 "Salesforce/LLaMA-3-8B-SFR-Iterative-DPO-R" \
    --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": "Salesforce/LLaMA-3-8B-SFR-Iterative-DPO-R",
		"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 "Salesforce/LLaMA-3-8B-SFR-Iterative-DPO-R" \
        --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": "Salesforce/LLaMA-3-8B-SFR-Iterative-DPO-R",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Salesforce/LLaMA-3-8B-SFR-Iterative-DPO-R with Docker Model Runner:
```
docker model run hf.co/Salesforce/LLaMA-3-8B-SFR-Iterative-DPO-R
```

bpucla commited on Jun 12, 2024

Commit

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@@ -34,7 +34,7 @@ For a detailed exposition, please refer to our accompanying technical report.
 | **Ours**                |          |                   |                       |              |                     |
 | Ours (SFT baseline)     | 8B       | SFT               | 10.2                  | 7.69         | 5.6                 |
 | Ours (DPO baseline)     | 8B       | Vanilla DPO       | 22.5                  | 8.17         | 22.4                |
-| Ours (Online RLHF)      | 8B       | Iterative DPO     | **37.2**              | **8.46**     | **29.1**            |
 | **Large Open-Sourced Models**       |          |                   |                       |              |                     |
 | Vicuna-33b-v1.3         | 33B      | SFT               | 17.6                  | 7.12         | 8.6                 |
 | Yi-34B-Chat             | 34B      | SFT               | 27.2                  | -            | 23.1                |

 | **Ours**                |          |                   |                       |              |                     |
 | Ours (SFT baseline)     | 8B       | SFT               | 10.2                  | 7.69         | 5.6                 |
 | Ours (DPO baseline)     | 8B       | Vanilla DPO       | 22.5                  | 8.17         | 22.4                |
+| Ours (Online RLHF)      | 8B       | Iterative DPO     | **31.3**              | **8.46**     | **29.1**            |
 | **Large Open-Sourced Models**       |          |                   |                       |              |                     |
 | Vicuna-33b-v1.3         | 33B      | SFT               | 17.6                  | 7.12         | 8.6                 |
 | Yi-34B-Chat             | 34B      | SFT               | 27.2                  | -            | 23.1                |