Instructions to use usmanxia/llama_3_1_8B_Pak_DPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use usmanxia/llama_3_1_8B_Pak_DPO with Transformers:

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

pipe = pipeline("text-generation", model="usmanxia/llama_3_1_8B_Pak_DPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("usmanxia/llama_3_1_8B_Pak_DPO")
model = AutoModelForCausalLM.from_pretrained("usmanxia/llama_3_1_8B_Pak_DPO")
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 usmanxia/llama_3_1_8B_Pak_DPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "usmanxia/llama_3_1_8B_Pak_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": "usmanxia/llama_3_1_8B_Pak_DPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/usmanxia/llama_3_1_8B_Pak_DPO

SGLang

How to use usmanxia/llama_3_1_8B_Pak_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 "usmanxia/llama_3_1_8B_Pak_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": "usmanxia/llama_3_1_8B_Pak_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 "usmanxia/llama_3_1_8B_Pak_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": "usmanxia/llama_3_1_8B_Pak_DPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use usmanxia/llama_3_1_8B_Pak_DPO with Docker Model Runner:
```
docker model run hf.co/usmanxia/llama_3_1_8B_Pak_DPO
```

llama3_1_8B_dpo_newdata_full_checkpoints

This model is a fine-tuned version of ramzanniaz331/llama3-8b-full-sft-v3 on the dpo_dataset_1, the dpo_dataset_2, the dpo_dataset_3, the dpo_dataset_4, the dpo_dataset_5 and the dpo_dataset_6 datasets. It achieves the following results on the evaluation set:

Loss: 0.1399
Rewards/chosen: -4.0691
Rewards/rejected: -62.5231
Rewards/accuracies: 0.9375
Rewards/margins: 58.4540
Logps/chosen: -299.7252
Logps/rejected: -1401.1368
Logits/chosen: -1.4589
Logits/rejected: -1.4782

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-06
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 8
total_train_batch_size: 64
total_eval_batch_size: 8
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_steps: 0.05
num_epochs: 1.0

Training results

Framework versions

Transformers 5.0.0.dev0
Pytorch 2.9.1+cu128
Datasets 4.0.0
Tokenizers 0.22.2

Downloads last month: 5

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for usmanxia/llama_3_1_8B_Pak_DPO

Base model

meta-llama/Llama-3.1-8B

Finetuned

ramzanniaz331/llama3.1-8b-8192-v3

Finetuned

ramzanniaz331/llama3-8b-full-sft-v3

Finetuned

(3)

this model