Instructions to use jackf857/llama-3-8b-base-margin-dpo-hh-helpful-batch-64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jackf857/llama-3-8b-base-margin-dpo-hh-helpful-batch-64 with Transformers:

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

pipe = pipeline("text-generation", model="jackf857/llama-3-8b-base-margin-dpo-hh-helpful-batch-64")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jackf857/llama-3-8b-base-margin-dpo-hh-helpful-batch-64")
model = AutoModelForCausalLM.from_pretrained("jackf857/llama-3-8b-base-margin-dpo-hh-helpful-batch-64", 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 jackf857/llama-3-8b-base-margin-dpo-hh-helpful-batch-64 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jackf857/llama-3-8b-base-margin-dpo-hh-helpful-batch-64"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jackf857/llama-3-8b-base-margin-dpo-hh-helpful-batch-64",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jackf857/llama-3-8b-base-margin-dpo-hh-helpful-batch-64

SGLang

How to use jackf857/llama-3-8b-base-margin-dpo-hh-helpful-batch-64 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 "jackf857/llama-3-8b-base-margin-dpo-hh-helpful-batch-64" \
    --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": "jackf857/llama-3-8b-base-margin-dpo-hh-helpful-batch-64",
		"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 "jackf857/llama-3-8b-base-margin-dpo-hh-helpful-batch-64" \
        --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": "jackf857/llama-3-8b-base-margin-dpo-hh-helpful-batch-64",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jackf857/llama-3-8b-base-margin-dpo-hh-helpful-batch-64 with Docker Model Runner:
```
docker model run hf.co/jackf857/llama-3-8b-base-margin-dpo-hh-helpful-batch-64
```

llama-3-8b-base-margin-dpo-hh-helpful-4xH200-batch-64

This model is a fine-tuned version of W-61/llama-3-8b-base-sft-hh-helpful-4xh200 on the Anthropic/hh-rlhf dataset. It achieves the following results on the evaluation set:

Loss: 0.4046
Margin Dpo/beta: 0.1000
Margin Dpo/loss Margin Mean: 21.7563
Margin Dpo/beta Margin Mean: 2.1756
Margin Dpo/beta Margin Grad Mean: -0.2570
Margin Dpo/beta Margin Grad Std: 0.2538
Margin Dpo/margin Mean: 21.7563
Margin Dpo/margin Std: 26.3378
Logps/chosen: -105.9372
Logps/rejected: -135.4405
Logps/ref Chosen: -79.0510
Logps/ref Rejected: -86.7979
Logits/chosen: -0.6270
Logits/rejected: -0.6013

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: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 2
total_train_batch_size: 64
total_eval_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH 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

Training results

Training Loss	Epoch	Step	Validation Loss	Margin Dpo/beta	Margin Dpo/loss Margin Mean	Margin Dpo/beta Margin Mean	Margin Dpo/beta Margin Grad Mean	Margin Dpo/beta Margin Grad Std	Margin Dpo/margin Mean	Margin Dpo/margin Std	Logps/chosen	Logps/rejected	Logps/ref Chosen	Logps/ref Rejected	Logits/chosen	Logits/rejected
0.9037	0.1468	100	0.5593	0.1000	8.4400	0.8440	-0.3668	0.2303	8.4400	15.3426	-87.1427	-103.3296	-79.0510	-86.7979	-0.6628	-0.6366
0.6607	0.2937	200	0.4791	0.1000	14.6826	1.4683	-0.3109	0.2473	14.6826	21.1628	-92.9979	-115.4274	-79.0510	-86.7979	-0.6426	-0.6197
0.699	0.4405	300	0.4414	0.1000	18.1032	1.8103	-0.2828	0.2516	18.1032	23.7825	-99.9692	-125.8193	-79.0510	-86.7979	-0.6107	-0.5845
0.4468	0.5874	400	0.4213	0.1000	20.2783	2.0278	-0.2687	0.2540	20.2783	25.4582	-102.0468	-130.0720	-79.0510	-86.7979	-0.5647	-0.5335
0.38	0.7342	500	0.4098	0.1000	21.8238	2.1824	-0.2579	0.2561	21.8238	26.5974	-106.9358	-136.5065	-79.0510	-86.7979	-0.6236	-0.5976
0.4876	0.8811	600	0.4046	0.1000	21.7563	2.1756	-0.2570	0.2538	21.7563	26.3378	-105.9372	-135.4405	-79.0510	-86.7979	-0.6270	-0.6013

Framework versions

Transformers 4.51.0
Pytorch 2.3.1+cu121
Datasets 2.21.0
Tokenizers 0.21.4

Downloads last month: 5

Safetensors

Model size

8B params

Tensor type

F32

Model tree for jackf857/llama-3-8b-base-margin-dpo-hh-helpful-batch-64

Base model

meta-llama/Meta-Llama-3-8B

Finetuned

W-61/llama-3-8b-base-sft-hh-helpful-4xh200

Finetuned

(28)

this model

jackf857
/

llama-3-8b-base-margin-dpo-hh-helpful-batch-64

llama-3-8b-base-margin-dpo-hh-helpful-4xH200-batch-64

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for jackf857/llama-3-8b-base-margin-dpo-hh-helpful-batch-64

Dataset used to train jackf857/llama-3-8b-base-margin-dpo-hh-helpful-batch-64