Instructions to use jackf857/llama-3-8b-base-kto-ultrafeedback-8xh200 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jackf857/llama-3-8b-base-kto-ultrafeedback-8xh200 with Transformers:

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

pipe = pipeline("text-generation", model="jackf857/llama-3-8b-base-kto-ultrafeedback-8xh200")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jackf857/llama-3-8b-base-kto-ultrafeedback-8xh200")
model = AutoModelForCausalLM.from_pretrained("jackf857/llama-3-8b-base-kto-ultrafeedback-8xh200", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jackf857/llama-3-8b-base-kto-ultrafeedback-8xh200 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jackf857/llama-3-8b-base-kto-ultrafeedback-8xh200"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jackf857/llama-3-8b-base-kto-ultrafeedback-8xh200",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jackf857/llama-3-8b-base-kto-ultrafeedback-8xh200

SGLang

How to use jackf857/llama-3-8b-base-kto-ultrafeedback-8xh200 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 "jackf857/llama-3-8b-base-kto-ultrafeedback-8xh200" \
    --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": "jackf857/llama-3-8b-base-kto-ultrafeedback-8xh200",
		"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 "jackf857/llama-3-8b-base-kto-ultrafeedback-8xh200" \
        --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": "jackf857/llama-3-8b-base-kto-ultrafeedback-8xh200",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jackf857/llama-3-8b-base-kto-ultrafeedback-8xh200 with Docker Model Runner:
```
docker model run hf.co/jackf857/llama-3-8b-base-kto-ultrafeedback-8xh200
```

llama-3-8b-base-kto-ultrafeedback-8xh200

File size: 512 Bytes

c3987f1

{
    "epoch": 1.0,
    "eval_kl": 0.0,
    "eval_logits/chosen": -141123088.0,
    "eval_logits/rejected": -139890512.0,
    "eval_logps/chosen": -287.08896875,
    "eval_logps/rejected": -293.79315625,
    "eval_loss": 0.3648396134376526,
    "eval_rewards/chosen": 0.0766905059814453,
    "eval_rewards/margins": 2.760806716918945,
    "eval_rewards/rejected": -2.6841162109375,
    "eval_runtime": 92.6279,
    "eval_samples": 4000,
    "eval_samples_per_second": 43.184,
    "eval_steps_per_second": 1.349
}