Instructions to use tanliboy/lambda-gemma-2-9b-dpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tanliboy/lambda-gemma-2-9b-dpo with Transformers:

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

pipe = pipeline("text-generation", model="tanliboy/lambda-gemma-2-9b-dpo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tanliboy/lambda-gemma-2-9b-dpo")
model = AutoModelForCausalLM.from_pretrained("tanliboy/lambda-gemma-2-9b-dpo", device_map="auto")
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 Settings

vLLM

How to use tanliboy/lambda-gemma-2-9b-dpo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tanliboy/lambda-gemma-2-9b-dpo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tanliboy/lambda-gemma-2-9b-dpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tanliboy/lambda-gemma-2-9b-dpo

SGLang

How to use tanliboy/lambda-gemma-2-9b-dpo 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 "tanliboy/lambda-gemma-2-9b-dpo" \
    --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": "tanliboy/lambda-gemma-2-9b-dpo",
		"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 "tanliboy/lambda-gemma-2-9b-dpo" \
        --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": "tanliboy/lambda-gemma-2-9b-dpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tanliboy/lambda-gemma-2-9b-dpo with Docker Model Runner:
```
docker model run hf.co/tanliboy/lambda-gemma-2-9b-dpo
```

lambda-gemma-2-9b-dpo / README.md

tanliboy

End of training

b141471 verified about 2 years ago

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metadata

license: gemma
base_model: tanliboy/zephyr-gemma-2-9b-sft
tags:
  - alignment-handbook
  - trl
  - dpo
  - generated_from_trainer
  - trl
  - dpo
  - generated_from_trainer
datasets:
  - HuggingFaceH4/ultrafeedback_binarized
model-index:
  - name: zephyr-gemma-2-9b-dpo-2
    results: []

zephyr-gemma-2-9b-dpo-2

This model is a fine-tuned version of tanliboy/zephyr-gemma-2-9b-sft on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

Loss: 0.5628
Rewards/chosen: -0.7292
Rewards/rejected: -1.2825
Rewards/accuracies: 0.6960
Rewards/margins: 0.5533
Logps/rejected: -1566.9301
Logps/chosen: -1043.5624
Logits/rejected: -14.1720
Logits/chosen: -14.6638

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-07
train_batch_size: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 16
total_train_batch_size: 256
total_eval_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.2
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.6835	0.2094	50	0.6815	-0.0218	-0.0436	0.6560	0.0218	-328.0053	-336.0947	-11.6381	-11.3403
0.6243	0.4187	100	0.6229	-0.5238	-0.7528	0.6600	0.2290	-1037.2136	-838.1255	-15.5098	-15.6787
0.5625	0.6281	150	0.5793	-0.7186	-1.1873	0.6880	0.4688	-1471.7362	-1032.8834	-14.7746	-15.1797
0.5699	0.8375	200	0.5647	-0.6443	-1.1499	0.6920	0.5057	-1434.3335	-958.5825	-14.1861	-14.6684

Framework versions

Transformers 4.43.1
Pytorch 2.3.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1