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
```

doyensahoo

hendrydong commited on Jan 21, 2025

Commit

46af8d2

verified ·

1 Parent(s): c0c2caa

Update README.md (#1)

Browse files

- Update README.md (82162616d8a04544dc7a4580d86115e10041d622)

Co-authored-by: Hanze Dong <hendrydong@users.noreply.huggingface.co>

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@@ -5,3 +5,16 @@ license: llama3
 # 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.

 # 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](https://www.salesforce.com/content/dam/web/en_us/www/documents/legal/Agreements/policies/ExternalFacing_Services_Policy.pdf)
+and [AI AUP](https://www.salesforce.com/content/dam/web/en_us/www/documents/legal/Agreements/policies/ai-acceptable-use-policy.pdf).