Text Generation
Transformers
Russian
English
llama-3.2
personalization
russian
telegram
lora
tpu
conversational-ai
fine-tuned
conversational
Instructions to use Aze4ka/projectme with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aze4ka/projectme with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Aze4ka/projectme") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Aze4ka/projectme", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Aze4ka/projectme with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Aze4ka/projectme" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Aze4ka/projectme", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Aze4ka/projectme
- SGLang
How to use Aze4ka/projectme 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 "Aze4ka/projectme" \ --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": "Aze4ka/projectme", "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 "Aze4ka/projectme" \ --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": "Aze4ka/projectme", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Aze4ka/projectme with Docker Model Runner:
docker model run hf.co/Aze4ka/projectme
| { | |
| "model_name": "meta-llama/Llama-3.2-3B-Instruct", | |
| "task_type": "CAUSAL_LM", | |
| "training_parameters": { | |
| "num_epochs": 1, | |
| "batch_size": 8, | |
| "learning_rate": 0.0002, | |
| "max_length": 1024, | |
| "num_warmup_steps": 128 | |
| }, | |
| "lora_config": { | |
| "r": 16, | |
| "lora_alpha": 32, | |
| "lora_dropout": 0.1, | |
| "bias": "none", | |
| "target_modules": [ | |
| "q_proj", | |
| "k_proj", | |
| "v_proj", | |
| "o_proj", | |
| "gate_proj", | |
| "up_proj" | |
| ] | |
| }, | |
| "training_results": { | |
| "final_training_loss": 2.3811, | |
| "final_validation_loss": 2.2514, | |
| "final_training_perplexity": 10.82, | |
| "final_validation_perplexity": 9.5, | |
| "total_steps": 8012, | |
| "training_time_hours": 8.1 | |
| }, | |
| "hardware": { | |
| "platform": "TPU v3-8", | |
| "framework": "PyTorch + torch_xla", | |
| "environment": "Kaggle" | |
| } | |
| } |