Instructions to use tanliboy/lambda-llama-3-8b-dpo-test-orca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tanliboy/lambda-llama-3-8b-dpo-test-orca with Transformers:

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

pipe = pipeline("text-generation", model="tanliboy/lambda-llama-3-8b-dpo-test-orca")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tanliboy/lambda-llama-3-8b-dpo-test-orca")
model = AutoModelForCausalLM.from_pretrained("tanliboy/lambda-llama-3-8b-dpo-test-orca")
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 tanliboy/lambda-llama-3-8b-dpo-test-orca with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tanliboy/lambda-llama-3-8b-dpo-test-orca"
# 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-llama-3-8b-dpo-test-orca",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tanliboy/lambda-llama-3-8b-dpo-test-orca

SGLang

How to use tanliboy/lambda-llama-3-8b-dpo-test-orca 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-llama-3-8b-dpo-test-orca" \
    --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-llama-3-8b-dpo-test-orca",
		"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-llama-3-8b-dpo-test-orca" \
        --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-llama-3-8b-dpo-test-orca",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tanliboy/lambda-llama-3-8b-dpo-test-orca with Docker Model Runner:
```
docker model run hf.co/tanliboy/lambda-llama-3-8b-dpo-test-orca
```

lambda-llama-3-8b-dpo-test-orca

This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct on the HuggingFaceH4/ultrafeedback_binarized and the tanliboy/orca_dpo_pairs datasets. It achieves the following results on the evaluation set:

Loss: 0.4795
Rewards/chosen: -1.6860
Rewards/rejected: -2.8132
Rewards/accuracies: 0.7259
Rewards/margins: 1.1272
Logps/rejected: -645.5051
Logps/chosen: -549.3651
Logits/rejected: -2.6630
Logits/chosen: -2.5985

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: 2e-07
train_batch_size: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 4
total_train_batch_size: 128
total_eval_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
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
0.6011	0.1744	100	0.5738	-0.8770	-1.2808	0.6988	0.4038	-492.2603	-468.4565	-2.4544	-2.4042
0.5447	0.3489	200	0.5242	-1.3236	-2.0879	0.7289	0.7644	-572.9752	-513.1177	-2.6319	-2.5732
0.5173	0.5233	300	0.5003	-1.6828	-2.6810	0.7259	0.9982	-632.2809	-549.0404	-2.6140	-2.5556
0.5144	0.6978	400	0.4851	-1.7107	-2.8135	0.7319	1.1028	-645.5279	-551.8306	-2.7027	-2.6365
0.5162	0.8722	500	0.4798	-1.7085	-2.8440	0.7259	1.1355	-648.5815	-551.6072	-2.6442	-2.5812

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

Downloads last month: -

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for tanliboy/lambda-llama-3-8b-dpo-test-orca

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Finetuned

(2750)

this model

tanliboy
/

lambda-llama-3-8b-dpo-test-orca

lambda-llama-3-8b-dpo-test-orca

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for tanliboy/lambda-llama-3-8b-dpo-test-orca

Datasets used to train tanliboy/lambda-llama-3-8b-dpo-test-orca