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

Libraries

How to use jackf857/llama-3-8b-base-simpo-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-simpo-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-simpo-8xh200")
model = AutoModelForCausalLM.from_pretrained("jackf857/llama-3-8b-base-simpo-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-simpo-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-simpo-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-simpo-8xh200",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

llama-3-8b-base-simpo-8xh200

File size: 781 Bytes

d8cc203

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    "epoch": 0.9989528795811519,
    "eval_logits/chosen": -0.7624644637107849,
    "eval_logits/rejected": -0.7490274310112,
    "eval_logps/chosen": -1.5084009170532227,
    "eval_logps/rejected": -2.0472562313079834,
    "eval_loss": 1.0240256786346436,
    "eval_rewards/accuracies": 0.7419354915618896,
    "eval_rewards/chosen": -3.0168018341064453,
    "eval_rewards/margins": 1.0777103900909424,
    "eval_rewards/rejected": -4.094512462615967,
    "eval_runtime": 24.4713,
    "eval_samples": 2000,
    "eval_samples_per_second": 81.728,
    "eval_steps_per_second": 1.308,
    "total_flos": 0.0,
    "train_loss": 2.230338767889411,
    "train_runtime": 2802.8273,
    "train_samples": 61135,
    "train_samples_per_second": 21.812,
    "train_steps_per_second": 0.17
}