Instructions to use allenai/tulu-2-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use allenai/tulu-2-13b with Transformers:

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

pipe = pipeline("text-generation", model="allenai/tulu-2-13b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("allenai/tulu-2-13b")
model = AutoModelForCausalLM.from_pretrained("allenai/tulu-2-13b")
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 allenai/tulu-2-13b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "allenai/tulu-2-13b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "allenai/tulu-2-13b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/allenai/tulu-2-13b

SGLang

How to use allenai/tulu-2-13b 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 "allenai/tulu-2-13b" \
    --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": "allenai/tulu-2-13b",
		"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 "allenai/tulu-2-13b" \
        --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": "allenai/tulu-2-13b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use allenai/tulu-2-13b with Docker Model Runner:
```
docker model run hf.co/allenai/tulu-2-13b
```

hamishivi commited on Nov 20, 2023

Commit

27c3480

1 Parent(s): e06acc8

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@@ -37,27 +37,12 @@ For more details, read the paper: [Camels in a Changing Climate: Enhancing LM Ad
 | Model | Size | Alignment | MT-Bench (score) | AlpacaEval (win rate %) |
 |-------------|-----|----|---------------|--------------|
-| **Tulu-v2-7b** 🐪 | **7B** | **dDPO** | **6.30** | **73.9** |
-| **Tulu-v2-dpo-7b** 🐪 | **7B** | **dDPO** | **6.29** | **85.1** |
-| StableLM-Tuned-α | 7B| dSFT |2.75| -|
-| MPT-Chat |  7B |dSFT |5.42| -|
-| Xwin-LMv0.1 | 7B| dPPO| 6.19| 87.83|
-| Mistral-Instructv0.1 | 7B|  - | 6.84 |-|
-| Zephyr-7b-α |7B|  dDPO| 6.88| -|
-| Zephyr-7b-β 🪁 | 7B | dDPO | 7.34 | 90.60 |
-| **Tulu-v2-13b** 🐪 | **13B** | **dDPO** | **6.70** | **78.9** |
-| **Tulu-v2-dpo-13b** 🐪 | **13B** | **dDPO** | **7.00** | **89.5** |
-| Falcon-Instruct |  40B |dSFT |5.17 |45.71|
-| Guanaco | 65B |  SFT |6.41| 71.80|
-| Llama2-Chat |  70B |RLHF |6.86| 92.66|
-| Vicuna v1.3 |  33B |dSFT |7.12 |88.99|
-| WizardLM v1.0 |  70B |dSFT |7.71 |-|
-| Xwin-LM v0.1 |   70B |dPPO |- |95.57|
-| **Tulu-v2-70b** 🐪 | **70B** | **dDPO** | **7.49** | **86.6** |
-| **Tulu-v2-dpo-70b** 🐪 | **70B** | **dDPO** | **7.89** | **95.1** |
-| GPT-3.5-turbo | - |RLHF |7.94 |89.37|
-| Claude 2 |  - |RLHF |8.06| 91.36|
-| GPT-4 |  -| RLHF |8.99| 95.28|
 ## Input Format

 | Model | Size | Alignment | MT-Bench (score) | AlpacaEval (win rate %) |
 |-------------|-----|----|---------------|--------------|
+| **Tulu-v2-7b** 🐪 | **7B** | **SFT** | **6.30** | **73.9** |
+| **Tulu-v2-dpo-7b** 🐪 | **7B** | **DPO** | **6.29** | **85.1** |
+| **Tulu-v2-13b** 🐪 | **13B** | **SFT** | **6.70** | **78.9** |
+| **Tulu-v2-dpo-13b** 🐪 | **13B** | **DPO** | **7.00** | **89.5** |
+| **Tulu-v2-70b** 🐪 | **70B** | **SFT** | **7.49** | **86.6** |
+| **Tulu-v2-dpo-70b** 🐪 | **70B** | **DPO** | **7.89** | **95.1** |
 ## Input Format