Text Generation
Transformers
Safetensors
gpt_neox
axolotl
Generated from Trainer
conversational
text-generation-inference
Instructions to use SystemAdmin123/pythia-14m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SystemAdmin123/pythia-14m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SystemAdmin123/pythia-14m") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("SystemAdmin123/pythia-14m") model = AutoModelForCausalLM.from_pretrained("SystemAdmin123/pythia-14m") 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=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use SystemAdmin123/pythia-14m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SystemAdmin123/pythia-14m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SystemAdmin123/pythia-14m", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/SystemAdmin123/pythia-14m
- SGLang
How to use SystemAdmin123/pythia-14m 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 "SystemAdmin123/pythia-14m" \ --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": "SystemAdmin123/pythia-14m", "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 "SystemAdmin123/pythia-14m" \ --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": "SystemAdmin123/pythia-14m", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use SystemAdmin123/pythia-14m with Docker Model Runner:
docker model run hf.co/SystemAdmin123/pythia-14m
| library_name: transformers | |
| base_model: EleutherAI/pythia-14m | |
| tags: | |
| - axolotl | |
| - generated_from_trainer | |
| datasets: | |
| - argilla/databricks-dolly-15k-curated-en | |
| model-index: | |
| - name: pythia-14m | |
| 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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) | |
| <details><summary>See axolotl config</summary> | |
| axolotl version: `0.6.0` | |
| ```yaml | |
| base_model: EleutherAI/pythia-14m | |
| batch_size: 128 | |
| bf16: true | |
| chat_template: tokenizer_default_fallback_alpaca | |
| datasets: | |
| - format: custom | |
| path: argilla/databricks-dolly-15k-curated-en | |
| type: | |
| field_input: original-instruction | |
| field_instruction: original-instruction | |
| field_output: original-response | |
| format: '{instruction} {input}' | |
| no_input_format: '{instruction}' | |
| system_format: '{system}' | |
| system_prompt: '' | |
| device_map: auto | |
| eval_sample_packing: false | |
| eval_steps: 20 | |
| flash_attention: true | |
| gradient_checkpointing: true | |
| group_by_length: true | |
| hub_model_id: SystemAdmin123/pythia-14m | |
| hub_strategy: checkpoint | |
| learning_rate: 0.0002 | |
| logging_steps: 10 | |
| lr_scheduler: cosine | |
| max_steps: 10000 | |
| micro_batch_size: 32 | |
| model_type: AutoModelForCausalLM | |
| num_epochs: 100 | |
| optimizer: adamw_bnb_8bit | |
| output_dir: /root/.sn56/axolotl/tmp/pythia-14m | |
| pad_to_sequence_len: true | |
| resize_token_embeddings_to_32x: false | |
| sample_packing: true | |
| save_steps: 20 | |
| save_total_limit: 1 | |
| sequence_len: 2048 | |
| special_tokens: | |
| pad_token: <|endoftext|> | |
| tokenizer_type: GPTNeoXTokenizerFast | |
| torch_dtype: bf16 | |
| training_args_kwargs: | |
| hub_private_repo: true | |
| trust_remote_code: true | |
| val_set_size: 0.1 | |
| wandb_entity: '' | |
| wandb_mode: online | |
| wandb_name: EleutherAI/pythia-14m-argilla/databricks-dolly-15k-curated-en | |
| wandb_project: Gradients-On-Demand | |
| wandb_run: your_name | |
| wandb_runid: default | |
| warmup_ratio: 0.05 | |
| ``` | |
| </details><br> | |
| # pythia-14m | |
| This model is a fine-tuned version of [EleutherAI/pythia-14m](https://huggingface.co/EleutherAI/pythia-14m) on the argilla/databricks-dolly-15k-curated-en dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 8.8742 | |
| ## 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: 32 | |
| - eval_batch_size: 32 | |
| - seed: 42 | |
| - distributed_type: multi-GPU | |
| - num_devices: 4 | |
| - total_train_batch_size: 128 | |
| - total_eval_batch_size: 128 | |
| - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_steps: 5 | |
| - training_steps: 100 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-------:|:----:|:---------------:| | |
| | No log | 0.1667 | 1 | 6.8294 | | |
| | 10.057 | 3.3333 | 20 | 8.6254 | | |
| | 9.7292 | 6.6667 | 40 | 8.7978 | | |
| | 9.1399 | 10.0 | 60 | 8.8257 | | |
| | 8.9523 | 13.3333 | 80 | 8.9145 | | |
| | 8.9387 | 16.6667 | 100 | 8.8742 | | |
| ### Framework versions | |
| - Transformers 4.48.1 | |
| - Pytorch 2.5.1+cu124 | |
| - Datasets 3.2.0 | |
| - Tokenizers 0.21.0 | |