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
Upload training_config.json with huggingface_hub
Browse files- training_config.json +38 -0
training_config.json
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{
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"model_name": "meta-llama/Llama-3.2-3B-Instruct",
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"task_type": "CAUSAL_LM",
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"training_parameters": {
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"num_epochs": 1,
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"batch_size": 8,
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"learning_rate": 0.0002,
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"max_length": 1024,
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"num_warmup_steps": 128
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},
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"lora_config": {
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"r": 16,
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"lora_alpha": 32,
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"lora_dropout": 0.1,
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"bias": "none",
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"target_modules": [
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"q_proj",
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"k_proj",
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"v_proj",
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"o_proj",
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"gate_proj",
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"up_proj"
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]
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},
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"training_results": {
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"final_training_loss": 2.3811,
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"final_validation_loss": 2.2514,
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"final_training_perplexity": 10.82,
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"final_validation_perplexity": 9.5,
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"total_steps": 8012,
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"training_time_hours": 8.1
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},
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"hardware": {
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"platform": "TPU v3-8",
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"framework": "PyTorch + torch_xla",
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"environment": "Kaggle"
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}
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}
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