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 Settings

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 May 31, 2024

Commit

5358809

verified ·

1 Parent(s): 6ecb6b8

Update README.md

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@@ -1,10 +1,10 @@
 ---
-license: cc-by-nc-nd-3.0
 ---
-# SFR-Iterative-DPO-Llama-3-8B-R
 ## Introduction
-We release a state-of-the-art instruct model of its class, **SFR-Iterative-DPO-LLaMA-3-8B-R**.
 On all three widely-used instruct model benchmarks: **Alpaca-Eval-V2**, **MT-Bench**, **Chat-Arena-Hard**, our model outperforms all models of similar size (e.g., LLaMA-3-8B-it), most large open-sourced models (e.g., Mixtral-8x7B-it),
 and strong proprietary models (e.g., GPT-3.5-turbo-0613). The model is trained with open-sourced datasets without any additional human-/GPT4-labeling.
@@ -66,8 +66,8 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 device = "cuda"
-model = AutoModelForCausalLM.from_pretrained("Salesforce/SFR-Iterative-DPO-LLaMA-3-8B-R")
-tokenizer = AutoTokenizer.from_pretrained("Salesforce/SFR-Iterative-DPO-LLaMA-3-8B-R")
 messages = [
     {"role": "user", "content": "I'm trying to teach myself to have nicer handwriting. Can you help?"},
@@ -85,7 +85,7 @@ print(model_outputs[0])
 ## Limitations
-SFR-Iterative-DPO-LLaMA-3-8B-R is a research model developed as part of our RLHF initiative at Salesforce.
 While safety and ethical considerations are integral to our alignment process,
 there remains the possibility that the model could generate offensive or unethical content, particularly under adversarial conditions.
 We are committed to continuous improvement in our models to minimize such risks and encourage responsible usage.

 ---
+license: llama3
 ---
+# Llama-3-8B-SFR-Iterative-DPO-R
 ## Introduction
+We release a state-of-the-art instruct model of its class, **Llama-3-8B-SFR-Iterative-DPO-R**.
 On all three widely-used instruct model benchmarks: **Alpaca-Eval-V2**, **MT-Bench**, **Chat-Arena-Hard**, our model outperforms all models of similar size (e.g., LLaMA-3-8B-it), most large open-sourced models (e.g., Mixtral-8x7B-it),
 and strong proprietary models (e.g., GPT-3.5-turbo-0613). The model is trained with open-sourced datasets without any additional human-/GPT4-labeling.
 device = "cuda"
+model = AutoModelForCausalLM.from_pretrained("Salesforce/Llama-3-8B-SFR-Iterative-DPO-R")
+tokenizer = AutoTokenizer.from_pretrained("Salesforce/Llama-3-8B-SFR-Iterative-DPO-R")
 messages = [
     {"role": "user", "content": "I'm trying to teach myself to have nicer handwriting. Can you help?"},
 ## Limitations
+Llama-3-8B-SFR-Iterative-DPO-R is a research model developed as part of our RLHF initiative at Salesforce.
 While safety and ethical considerations are integral to our alignment process,
 there remains the possibility that the model could generate offensive or unethical content, particularly under adversarial conditions.
 We are committed to continuous improvement in our models to minimize such risks and encourage responsible usage.