Instructions to use Salesforce/LLaMA-3-8B-SFR-Iterative-DPO-Concise-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-Concise-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-Concise-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-Concise-R")
model = AutoModelForCausalLM.from_pretrained("Salesforce/LLaMA-3-8B-SFR-Iterative-DPO-Concise-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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Salesforce/LLaMA-3-8B-SFR-Iterative-DPO-Concise-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-Concise-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-Concise-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-Concise-R

SGLang

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

Docker Model Runner
How to use Salesforce/LLaMA-3-8B-SFR-Iterative-DPO-Concise-R with Docker Model Runner:
```
docker model run hf.co/Salesforce/LLaMA-3-8B-SFR-Iterative-DPO-Concise-R
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

LLaMA-3-8B-SFR-Iterative-DPO-Concise-R

This is a concise version of Salesforce/SFR-Iterative-DPO-LLaMA-3-8B-R. In the training, a concise penalty is applied.

Ethics disclaimer for Salesforce AI models, data, code

This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our standard AUP and AI AUP.

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Safetensors

Model size

8B params

Tensor type

BF16

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Quantizations

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