Instructions to use adpretko/HyperstackTrainTest-armv8-O2-native-clang-02percent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use adpretko/HyperstackTrainTest-armv8-O2-native-clang-02percent with Transformers:

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

pipe = pipeline("text-generation", model="adpretko/HyperstackTrainTest-armv8-O2-native-clang-02percent")
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("adpretko/HyperstackTrainTest-armv8-O2-native-clang-02percent")
model = AutoModelForCausalLM.from_pretrained("adpretko/HyperstackTrainTest-armv8-O2-native-clang-02percent", 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 adpretko/HyperstackTrainTest-armv8-O2-native-clang-02percent with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "adpretko/HyperstackTrainTest-armv8-O2-native-clang-02percent"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "adpretko/HyperstackTrainTest-armv8-O2-native-clang-02percent",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/adpretko/HyperstackTrainTest-armv8-O2-native-clang-02percent

SGLang

How to use adpretko/HyperstackTrainTest-armv8-O2-native-clang-02percent 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 "adpretko/HyperstackTrainTest-armv8-O2-native-clang-02percent" \
    --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": "adpretko/HyperstackTrainTest-armv8-O2-native-clang-02percent",
		"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 "adpretko/HyperstackTrainTest-armv8-O2-native-clang-02percent" \
        --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": "adpretko/HyperstackTrainTest-armv8-O2-native-clang-02percent",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use adpretko/HyperstackTrainTest-armv8-O2-native-clang-02percent with Docker Model Runner:
```
docker model run hf.co/adpretko/HyperstackTrainTest-armv8-O2-native-clang-02percent
```

HyperstackTrainTest-armv8-O2-native-clang-02percent

File size: 2,236 Bytes

c7ca1a5

{
  "best_metric": null,
  "best_model_checkpoint": null,
  "epoch": 0.25161290322580643,
  "eval_steps": 500,
  "global_step": 78,
  "is_hyper_param_search": false,
  "is_local_process_zero": true,
  "is_world_process_zero": true,
  "log_history": [
    {
      "epoch": 0.03225806451612903,
      "grad_norm": 2.242180824279785,
      "learning_rate": 1.9959742939952393e-05,
      "loss": 0.492,
      "step": 10
    },
    {
      "epoch": 0.06451612903225806,
      "grad_norm": 0.8940081000328064,
      "learning_rate": 1.8584487936018663e-05,
      "loss": 0.1381,
      "step": 20
    },
    {
      "epoch": 0.0967741935483871,
      "grad_norm": 0.5671939849853516,
      "learning_rate": 1.5508969814521026e-05,
      "loss": 0.088,
      "step": 30
    },
    {
      "epoch": 0.12903225806451613,
      "grad_norm": 0.44803526997566223,
      "learning_rate": 1.1342332658176556e-05,
      "loss": 0.0739,
      "step": 40
    },
    {
      "epoch": 0.16129032258064516,
      "grad_norm": 0.34039735794067383,
      "learning_rate": 6.909830056250527e-06,
      "loss": 0.0676,
      "step": 50
    },
    {
      "epoch": 0.1935483870967742,
      "grad_norm": 0.3448280394077301,
      "learning_rate": 3.089373510131354e-06,
      "loss": 0.0611,
      "step": 60
    },
    {
      "epoch": 0.22580645161290322,
      "grad_norm": 0.3139958679676056,
      "learning_rate": 6.37651293602628e-07,
      "loss": 0.0583,
      "step": 70
    },
    {
      "epoch": 0.25161290322580643,
      "step": 78,
      "total_flos": 1.6179959995301888e+17,
      "train_loss": 0.13258298620199546,
      "train_runtime": 2183.8276,
      "train_samples_per_second": 2.264,
      "train_steps_per_second": 0.036
    }
  ],
  "logging_steps": 10,
  "max_steps": 78,
  "num_input_tokens_seen": 0,
  "num_train_epochs": 1,
  "save_steps": 1000,
  "stateful_callbacks": {
    "TrainerControl": {
      "args": {
        "should_epoch_stop": false,
        "should_evaluate": false,
        "should_log": false,
        "should_save": true,
        "should_training_stop": true
      },
      "attributes": {}
    }
  },
  "total_flos": 1.6179959995301888e+17,
  "train_batch_size": 8,
  "trial_name": null,
  "trial_params": null
}