Text Generation
Transformers
PyTorch
Safetensors
llama
Generated from Trainer
text-generation-inference
Instructions to use ethensanchez/MiniLlamaTest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ethensanchez/MiniLlamaTest with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ethensanchez/MiniLlamaTest")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ethensanchez/MiniLlamaTest") model = AutoModelForCausalLM.from_pretrained("ethensanchez/MiniLlamaTest", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ethensanchez/MiniLlamaTest with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ethensanchez/MiniLlamaTest" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ethensanchez/MiniLlamaTest", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ethensanchez/MiniLlamaTest
- SGLang
How to use ethensanchez/MiniLlamaTest 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 "ethensanchez/MiniLlamaTest" \ --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": "ethensanchez/MiniLlamaTest", "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 "ethensanchez/MiniLlamaTest" \ --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": "ethensanchez/MiniLlamaTest", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ethensanchez/MiniLlamaTest with Docker Model Runner:
docker model run hf.co/ethensanchez/MiniLlamaTest
| license: apache-2.0 | |
| base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: qlora-out | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) | |
| <details><summary>See axolotl config</summary> | |
| axolotl version: `0.4.0` | |
| ```yaml | |
| adapter: null | |
| base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T | |
| bf16: auto | |
| dataset_prepared_path: last_run_prepared | |
| datasets: | |
| - path: utrgvseniorproject/medtext | |
| type: completion | |
| debug: null | |
| deepspeed: null | |
| early_stopping_patience: null | |
| evals_per_epoch: null | |
| flash_attention: false | |
| fp16: null | |
| fsdp: null | |
| fsdp_config: null | |
| gradient_accumulation_steps: 1 | |
| gradient_checkpointing: true | |
| group_by_length: false | |
| is_llama_derived_model: true | |
| learning_rate: 0.0002 | |
| load_in_4bit: false | |
| load_in_8bit: false | |
| local_rank: null | |
| logging_steps: 1 | |
| lora_alpha: null | |
| lora_dropout: null | |
| lora_fan_in_fan_out: null | |
| lora_model_dir: null | |
| lora_r: null | |
| lora_target_linear: null | |
| lora_target_modules: null | |
| lr_scheduler: cosine | |
| micro_batch_size: 1 | |
| model_type: LlamaForCausalLM | |
| num_epochs: 2 | |
| optimizer: adamw_bnb_8bit | |
| output_dir: ./qlora-out | |
| pad_to_sequence_len: true | |
| resume_from_checkpoint: null | |
| sample_packing: true | |
| saves_per_epoch: null | |
| sequence_len: 1096 | |
| special_tokens: null | |
| strict: false | |
| tf32: false | |
| tokenizer_type: LlamaTokenizer | |
| train_on_inputs: false | |
| val_set_size: 0.05 | |
| wandb_entity: utrgvmedai | |
| wandb_log_model: true | |
| wandb_name: testingTiny | |
| wandb_project: TinyLlamaLLMTest | |
| wandb_watch: null | |
| warmup_steps: 10 | |
| weight_decay: 0.0 | |
| xformers_attention: null | |
| ``` | |
| </details><br> | |
| # qlora-out | |
| This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: nan | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 0.0002 | |
| - train_batch_size: 1 | |
| - eval_batch_size: 1 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_steps: 10 | |
| - num_epochs: 2 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 0.0 | 1.02 | 810 | nan | | |
| | 0.0 | 1.98 | 1594 | nan | | |
| ### Framework versions | |
| - Transformers 4.38.0.dev0 | |
| - Pytorch 2.1.2+cu121 | |
| - Datasets 2.17.0 | |
| - Tokenizers 0.15.0 | |