Instructions to use wxzhang/selective-pairrm-33045197-mt0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wxzhang/selective-pairrm-33045197-mt0 with Transformers:

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

pipe = pipeline("text-generation", model="wxzhang/selective-pairrm-33045197-mt0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("wxzhang/selective-pairrm-33045197-mt0")
model = AutoModelForCausalLM.from_pretrained("wxzhang/selective-pairrm-33045197-mt0")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use wxzhang/selective-pairrm-33045197-mt0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wxzhang/selective-pairrm-33045197-mt0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wxzhang/selective-pairrm-33045197-mt0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wxzhang/selective-pairrm-33045197-mt0

SGLang

How to use wxzhang/selective-pairrm-33045197-mt0 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 "wxzhang/selective-pairrm-33045197-mt0" \
    --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": "wxzhang/selective-pairrm-33045197-mt0",
		"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 "wxzhang/selective-pairrm-33045197-mt0" \
        --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": "wxzhang/selective-pairrm-33045197-mt0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use wxzhang/selective-pairrm-33045197-mt0 with Docker Model Runner:
```
docker model run hf.co/wxzhang/selective-pairrm-33045197-mt0
```

selective-pairrm-33045197-mt0

This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on the snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset dataset. It achieves the following results on the evaluation set:

Loss: 0.6825
Rewards/chosen: -0.2329
Rewards/rejected: -0.2692
Rewards/accuracies: 0.6055
Rewards/margins: 0.0362
Logps/rejected: -417.6746
Logps/chosen: -401.4102
Logits/rejected: -3.1643
Logits/chosen: -3.1708

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: 4
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 4
total_train_batch_size: 64
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
0.6786	0.32	100	0.6868	-0.0869	-0.1015	0.5547	0.0146	-400.9028	-386.8027	-2.8786	-2.8855
0.6615	0.64	200	0.6828	-0.1851	-0.2144	0.5938	0.0294	-412.2021	-396.6207	-3.0607	-3.0672
0.6539	0.96	300	0.6821	-0.2322	-0.2693	0.6055	0.0371	-417.6892	-401.3395	-3.1645	-3.1709

Framework versions

Transformers 4.36.2
Pytorch 2.1.2
Datasets 2.14.6
Tokenizers 0.15.0

Downloads last month: 5

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for wxzhang/selective-pairrm-33045197-mt0

Base model

mistralai/Mistral-7B-Instruct-v0.2

Finetuned

(1088)

this model

wxzhang
/

selective-pairrm-33045197-mt0

selective-pairrm-33045197-mt0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for wxzhang/selective-pairrm-33045197-mt0

Dataset used to train wxzhang/selective-pairrm-33045197-mt0