Instructions to use saiteja33/gemma2-2b-dpo-zh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use saiteja33/gemma2-2b-dpo-zh with Transformers:

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

pipe = pipeline("text-generation", model="saiteja33/gemma2-2b-dpo-zh")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("saiteja33/gemma2-2b-dpo-zh")
model = AutoModelForCausalLM.from_pretrained("saiteja33/gemma2-2b-dpo-zh")
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 saiteja33/gemma2-2b-dpo-zh with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "saiteja33/gemma2-2b-dpo-zh"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "saiteja33/gemma2-2b-dpo-zh",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/saiteja33/gemma2-2b-dpo-zh

SGLang

How to use saiteja33/gemma2-2b-dpo-zh 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 "saiteja33/gemma2-2b-dpo-zh" \
    --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": "saiteja33/gemma2-2b-dpo-zh",
		"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 "saiteja33/gemma2-2b-dpo-zh" \
        --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": "saiteja33/gemma2-2b-dpo-zh",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use saiteja33/gemma2-2b-dpo-zh with Docker Model Runner:
```
docker model run hf.co/saiteja33/gemma2-2b-dpo-zh
```

gemma2-2b-dpo-zh

This model is a fine-tuned version of saves/gemma2-2b-sft-zh on the gemma2_2b_train_dpo_zh dataset. It achieves the following results on the evaluation set:

Loss: 0.2930
Rewards/chosen: -6.2456
Rewards/rejected: -28.9376
Rewards/accuracies: 0.9433
Rewards/margins: 22.6921
Logps/chosen: -260.4318
Logps/rejected: -367.5450
Logits/chosen: -4.3646
Logits/rejected: -4.4725

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: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 4
total_train_batch_size: 8
total_eval_batch_size: 2
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/chosen	Logps/rejected	Logits/chosen	Logits/rejected
0.0513	0.3741	3000	0.3045	-4.9056	-25.4557	0.9369	20.5501	-247.0327	-332.7262	-4.2631	-4.2778
0.0185	0.7482	6000	0.2908	-6.0224	-29.2477	0.9424	23.2253	-258.2001	-370.6453	-4.4148	-4.5147

Framework versions

Transformers 4.57.1
Pytorch 2.11.0+cu126
Datasets 4.0.0
Tokenizers 0.22.2

Downloads last month: 27

Safetensors

Model size

242k params

Tensor type

BF16