Instructions to use DmitryYarov/aristotle_interface with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DmitryYarov/aristotle_interface with Transformers:

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

pipe = pipeline("text-generation", model="DmitryYarov/aristotle_interface")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DmitryYarov/aristotle_interface")
model = AutoModelForCausalLM.from_pretrained("DmitryYarov/aristotle_interface")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DmitryYarov/aristotle_interface with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DmitryYarov/aristotle_interface"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DmitryYarov/aristotle_interface",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DmitryYarov/aristotle_interface

SGLang

How to use DmitryYarov/aristotle_interface 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 "DmitryYarov/aristotle_interface" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DmitryYarov/aristotle_interface",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "DmitryYarov/aristotle_interface" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DmitryYarov/aristotle_interface",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DmitryYarov/aristotle_interface with Docker Model Runner:
```
docker model run hf.co/DmitryYarov/aristotle_interface
```

aristotle_interface

File size: 918 Bytes

410a5a0

{
  "best_metric": null,
  "best_model_checkpoint": null,
  "epoch": 29.864864864864863,
  "eval_steps": 1000,
  "global_step": 120,
  "is_hyper_param_search": false,
  "is_local_process_zero": true,
  "is_world_process_zero": true,
  "log_history": [
    {
      "epoch": 24.864864864864863,
      "grad_norm": 0.41308847069740295,
      "learning_rate": 6e-06,
      "loss": 3.8386,
      "step": 100
    }
  ],
  "logging_steps": 100,
  "max_steps": 120,
  "num_input_tokens_seen": 0,
  "num_train_epochs": 30,
  "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": 8754328240128000.0,
  "train_batch_size": 8,
  "trial_name": null,
  "trial_params": null
}