Instructions to use tanliboy/lambda-qwen2.5-14b-dpo-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tanliboy/lambda-qwen2.5-14b-dpo-test with Transformers:

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

pipe = pipeline("text-generation", model="tanliboy/lambda-qwen2.5-14b-dpo-test")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tanliboy/lambda-qwen2.5-14b-dpo-test")
model = AutoModelForCausalLM.from_pretrained("tanliboy/lambda-qwen2.5-14b-dpo-test")
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-qwen2.5-14b-dpo-test with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tanliboy/lambda-qwen2.5-14b-dpo-test"
# 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-qwen2.5-14b-dpo-test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tanliboy/lambda-qwen2.5-14b-dpo-test

SGLang

How to use tanliboy/lambda-qwen2.5-14b-dpo-test 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-qwen2.5-14b-dpo-test" \
    --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-qwen2.5-14b-dpo-test",
		"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-qwen2.5-14b-dpo-test" \
        --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-qwen2.5-14b-dpo-test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tanliboy/lambda-qwen2.5-14b-dpo-test with Docker Model Runner:
```
docker model run hf.co/tanliboy/lambda-qwen2.5-14b-dpo-test
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

lambda-qwen2.5-14b-dpo-test

This model is a fine-tuned version of Qwen/Qwen2.5-14B-Instruct on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

Loss: 0.4919
Rewards/chosen: -2.4745
Rewards/rejected: -3.3729
Rewards/accuracies: 0.7400
Rewards/margins: 0.8984
Logps/rejected: -832.0724
Logps/chosen: -737.5234
Logits/rejected: -1.2739
Logits/chosen: -1.2560

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: 8
total_train_batch_size: 128
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: 0.1
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
0.5269	0.2094	100	0.5333	-1.6756	-2.3320	0.7000	0.6564	-727.9815	-657.6356	-1.3952	-1.3850
0.5086	0.4187	200	0.5044	-2.0906	-2.9287	0.7040	0.8381	-787.6511	-699.1298	-1.2939	-1.2773
0.4787	0.6281	300	0.4948	-2.2927	-3.1689	0.7320	0.8762	-811.6696	-719.3386	-1.2846	-1.2646
0.4825	0.8375	400	0.4924	-2.4470	-3.3410	0.7400	0.8939	-828.8748	-734.7765	-1.2644	-1.2477

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

Downloads last month: 17

Safetensors

Model size

15B params

Tensor type

BF16

Model tree for tanliboy/lambda-qwen2.5-14b-dpo-test

Base model

Qwen/Qwen2.5-14B

Finetuned

Qwen/Qwen2.5-14B-Instruct

Finetuned

(390)

this model

Merges

19 models

Quantizations

3 models

tanliboy
/

lambda-qwen2.5-14b-dpo-test

lambda-qwen2.5-14b-dpo-test

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for tanliboy/lambda-qwen2.5-14b-dpo-test

Dataset used to train tanliboy/lambda-qwen2.5-14b-dpo-test

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