Instructions to use jz666/simpo-train-largest-30-abs-diff with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jz666/simpo-train-largest-30-abs-diff with Transformers:

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

pipe = pipeline("text-generation", model="jz666/simpo-train-largest-30-abs-diff")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jz666/simpo-train-largest-30-abs-diff")
model = AutoModelForCausalLM.from_pretrained("jz666/simpo-train-largest-30-abs-diff")
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 jz666/simpo-train-largest-30-abs-diff with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jz666/simpo-train-largest-30-abs-diff"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jz666/simpo-train-largest-30-abs-diff",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jz666/simpo-train-largest-30-abs-diff

SGLang

How to use jz666/simpo-train-largest-30-abs-diff 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 "jz666/simpo-train-largest-30-abs-diff" \
    --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": "jz666/simpo-train-largest-30-abs-diff",
		"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 "jz666/simpo-train-largest-30-abs-diff" \
        --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": "jz666/simpo-train-largest-30-abs-diff",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jz666/simpo-train-largest-30-abs-diff with Docker Model Runner:
```
docker model run hf.co/jz666/simpo-train-largest-30-abs-diff
```

simpo-train-largest-30-abs-diff / all_results.json

jz666

End of training

a97d445 verified 8 months ago

raw

history blame contribute delete

779 Bytes

	{
	"epoch": 0.995910949568378,
	"eval_logits/chosen": -6.920379161834717,
	"eval_logits/rejected": -6.706053733825684,
	"eval_logps/chosen": -0.6168988347053528,
	"eval_logps/rejected": -0.7138991951942444,
	"eval_loss": 4.313390731811523,
	"eval_rewards/accuracies": 0.6024590134620667,
	"eval_rewards/chosen": -6.168987274169922,
	"eval_rewards/margins": 0.9700047373771667,
	"eval_rewards/rejected": -7.138991832733154,
	"eval_runtime": 65.9401,
	"eval_samples": 1941,
	"eval_samples_per_second": 29.436,
	"eval_steps_per_second": 1.85,
	"total_flos": 0.0,
	"train_loss": 4.287031420826041,
	"train_runtime": 1653.4486,
	"train_samples": 17605,
	"train_samples_per_second": 10.647,
	"train_steps_per_second": 0.083
	}