Instructions to use alvarobartt/mistral-7b-orpo-alignment-handbook with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alvarobartt/mistral-7b-orpo-alignment-handbook with Transformers:

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

pipe = pipeline("text-generation", model="alvarobartt/mistral-7b-orpo-alignment-handbook")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alvarobartt/mistral-7b-orpo-alignment-handbook")
model = AutoModelForCausalLM.from_pretrained("alvarobartt/mistral-7b-orpo-alignment-handbook", 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-7b-orpo-alignment-handbook with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alvarobartt/mistral-7b-orpo-alignment-handbook"
# 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-7b-orpo-alignment-handbook",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/alvarobartt/mistral-7b-orpo-alignment-handbook

SGLang

How to use alvarobartt/mistral-7b-orpo-alignment-handbook 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-7b-orpo-alignment-handbook" \
    --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-7b-orpo-alignment-handbook",
		"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-7b-orpo-alignment-handbook" \
        --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-7b-orpo-alignment-handbook",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use alvarobartt/mistral-7b-orpo-alignment-handbook with Docker Model Runner:
```
docker model run hf.co/alvarobartt/mistral-7b-orpo-alignment-handbook
```

mistral-7b-orpo-alignment-handbook / README.md

alvarobartt HF Staff

End of training

4b440da verified over 2 years ago

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metadata

license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
  - alignment-handbook
  - trl
  - orpo
  - generated_from_trainer
  - trl
  - orpo
  - generated_from_trainer
datasets:
  - argilla/dpo-mix-7k
model-index:
  - name: mistral-7b-orpo-alignment-handbook
    results: []

mistral-7b-orpo-alignment-handbook

This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the argilla/dpo-mix-7k dataset. It achieves the following results on the evaluation set:

Loss: 0.8562
Rewards/chosen: -0.0394
Rewards/rejected: -0.0485
Rewards/accuracies: 0.6615
Rewards/margins: 0.0091
Logps/rejected: -0.9709
Logps/chosen: -0.7882
Logits/rejected: -2.9442
Logits/chosen: -2.9335
Nll Loss: 0.8317
Log Odds Ratio: -0.6241
Log Odds Chosen: 0.3600

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
0.9081	0.95	100	0.8756	-0.0406	-0.0483	0.625	0.0077	-0.9657	-0.8116	-3.0351	-3.0266	0.8517	-0.6438	0.3078
0.8743	1.9	200	0.8544	-0.0391	-0.0474	0.6458	0.0083	-0.9474	-0.7823	-2.9519	-2.9423	0.8308	-0.6319	0.3327
0.7952	2.84	300	0.8562	-0.0394	-0.0485	0.6615	0.0091	-0.9709	-0.7880	-2.9507	-2.9399	0.8317	-0.6238	0.3606

Framework versions

Transformers 4.39.0.dev0
Pytorch 2.2.1+cu121
Datasets 2.14.6
Tokenizers 0.15.2