Instructions to use alvarobartt/mistral-orpo-mix-b0.05-l1024-pl512-lr5e-7-cosine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alvarobartt/mistral-orpo-mix-b0.05-l1024-pl512-lr5e-7-cosine with Transformers:

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

pipe = pipeline("text-generation", model="alvarobartt/mistral-orpo-mix-b0.05-l1024-pl512-lr5e-7-cosine")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alvarobartt/mistral-orpo-mix-b0.05-l1024-pl512-lr5e-7-cosine")
model = AutoModelForCausalLM.from_pretrained("alvarobartt/mistral-orpo-mix-b0.05-l1024-pl512-lr5e-7-cosine", 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 alvarobartt/mistral-orpo-mix-b0.05-l1024-pl512-lr5e-7-cosine with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alvarobartt/mistral-orpo-mix-b0.05-l1024-pl512-lr5e-7-cosine"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alvarobartt/mistral-orpo-mix-b0.05-l1024-pl512-lr5e-7-cosine",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/alvarobartt/mistral-orpo-mix-b0.05-l1024-pl512-lr5e-7-cosine

SGLang

How to use alvarobartt/mistral-orpo-mix-b0.05-l1024-pl512-lr5e-7-cosine 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 "alvarobartt/mistral-orpo-mix-b0.05-l1024-pl512-lr5e-7-cosine" \
    --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": "alvarobartt/mistral-orpo-mix-b0.05-l1024-pl512-lr5e-7-cosine",
		"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 "alvarobartt/mistral-orpo-mix-b0.05-l1024-pl512-lr5e-7-cosine" \
        --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": "alvarobartt/mistral-orpo-mix-b0.05-l1024-pl512-lr5e-7-cosine",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use alvarobartt/mistral-orpo-mix-b0.05-l1024-pl512-lr5e-7-cosine with Docker Model Runner:
```
docker model run hf.co/alvarobartt/mistral-orpo-mix-b0.05-l1024-pl512-lr5e-7-cosine
```

mistral-orpo-mix-b0.05-l1024-pl512-lr5e-7-cosine

This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.8648
Rewards/chosen: -0.0405
Rewards/rejected: -0.0502
Rewards/accuracies: 0.6458
Rewards/margins: 0.0097
Logps/rejected: -1.0036
Logps/chosen: -0.8096
Logits/rejected: -2.9146
Logits/chosen: -2.9040
Nll Loss: 0.8392
Log Odds Ratio: -0.6215
Log Odds Chosen: 0.3802

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
lr_scheduler_warmup_steps: 100
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen	Nll Loss	Log Odds Ratio	Log Odds Chosen
0.9159	1.0	105	0.8794	-0.0421	-0.0499	0.6302	0.0078	-0.9975	-0.8413	-2.8931	-2.8875	0.8561	-0.6429	0.3024
0.8397	2.0	211	0.8612	-0.0404	-0.0495	0.6458	0.0092	-0.9902	-0.8071	-2.8882	-2.8794	0.8366	-0.6257	0.3555
0.7808	2.99	315	0.8648	-0.0405	-0.0502	0.6458	0.0097	-1.0036	-0.8096	-2.9146	-2.9040	0.8392	-0.6215	0.3802

Framework versions

Transformers 4.39.0
Pytorch 2.1.1+cu121
Datasets 2.16.1
Tokenizers 0.15.2

Downloads last month: 18

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for alvarobartt/mistral-orpo-mix-b0.05-l1024-pl512-lr5e-7-cosine

Base model

mistralai/Mistral-7B-v0.1

Finetuned

(949)

this model

Collection including alvarobartt/mistral-orpo-mix-b0.05-l1024-pl512-lr5e-7-cosine

About ORPO

Collection

Contains some information and experiments fine-tuning LLMs using 🤗 `trl.ORPOTrainer` • 7 items • Updated Mar 2 • 5