Text Generation
Transformers
PyTorch
Safetensors
llama
axolotl
Generated from Trainer
text-generation-inference
Instructions to use jeiku/completion4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jeiku/completion4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jeiku/completion4B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jeiku/completion4B") model = AutoModelForCausalLM.from_pretrained("jeiku/completion4B") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jeiku/completion4B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jeiku/completion4B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jeiku/completion4B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jeiku/completion4B
- SGLang
How to use jeiku/completion4B 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 "jeiku/completion4B" \ --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": "jeiku/completion4B", "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 "jeiku/completion4B" \ --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": "jeiku/completion4B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jeiku/completion4B with Docker Model Runner:
docker model run hf.co/jeiku/completion4B
| library_name: transformers | |
| license: other | |
| base_model: IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml | |
| tags: | |
| - axolotl | |
| - generated_from_trainer | |
| model-index: | |
| - name: completion4B | |
| 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: `0.4.1` | |
| ```yaml | |
| base_model: IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml | |
| model_type: AutoModelForCausalLM | |
| tokenizer_type: AutoTokenizer | |
| load_in_8bit: false | |
| load_in_4bit: false | |
| strict: false | |
| hub_model_id: jeiku/completion4B | |
| hub_strategy: "all_checkpoints" | |
| push_dataset_to_hub: | |
| hf_use_auth_token: true | |
| datasets: | |
| - path: Mielikki/Erebus-87k | |
| type: completion | |
| field: body | |
| shuffle_merged_datasets: true | |
| val_set_size: 0.0025 | |
| output_dir: ./outputs/out | |
| adapter: | |
| lora_r: | |
| lora_alpha: | |
| lora_dropout: | |
| lora_target_linear: | |
| sequence_len: 8192 | |
| sample_packing: true | |
| eval_sample_packing: false | |
| pad_to_sequence_len: true | |
| plugins: | |
| - axolotl.integrations.liger.LigerPlugin | |
| liger_rope: true | |
| liger_rms_norm: true | |
| liger_swiglu: true | |
| liger_fused_linear_cross_entropy: true | |
| wandb_project: EXP4B | |
| wandb_entity: | |
| wandb_watch: | |
| wandb_name: EXP4B | |
| wandb_log_model: | |
| gradient_accumulation_steps: 12 | |
| micro_batch_size: 3 | |
| num_epochs: 1 | |
| optimizer: adamw_bnb_8bit | |
| lr_scheduler: cosine | |
| learning_rate: 0.00001 | |
| weight_decay: 0.05 | |
| train_on_inputs: false | |
| group_by_length: false | |
| bf16: auto | |
| fp16: | |
| tf32: true | |
| gradient_checkpointing: true | |
| early_stopping_patience: | |
| resume_from_checkpoint: | |
| local_rank: | |
| logging_steps: 1 | |
| xformers_attention: | |
| flash_attention: true | |
| warmup_ratio: 0.1 | |
| evals_per_epoch: 4 | |
| eval_table_size: | |
| eval_max_new_tokens: 128 | |
| saves_per_epoch: 1 | |
| debug: | |
| deepspeed: deepspeed_configs/zero3_bf16.json | |
| fsdp: | |
| fsdp_config: | |
| special_tokens: | |
| pad_token: <|finetune_right_pad_id|> | |
| ``` | |
| </details><br> | |
| # completion4B | |
| This model is a fine-tuned version of [IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml](https://huggingface.co/IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 2.9360 | |
| ## 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: 1e-05 | |
| - train_batch_size: 3 | |
| - eval_batch_size: 3 | |
| - seed: 42 | |
| - distributed_type: multi-GPU | |
| - num_devices: 2 | |
| - gradient_accumulation_steps: 12 | |
| - total_train_batch_size: 72 | |
| - total_eval_batch_size: 6 | |
| - 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: 34 | |
| - num_epochs: 1 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:------:|:----:|:---------------:| | |
| | 2.5227 | 0.0029 | 1 | 2.9798 | | |
| | 2.5027 | 0.2520 | 88 | 2.9501 | | |
| | 2.481 | 0.5039 | 176 | 2.9398 | | |
| | 2.4313 | 0.7559 | 264 | 2.9360 | | |
| ### Framework versions | |
| - Transformers 4.46.0.dev0 | |
| - Pytorch 2.4.0+cu121 | |
| - Datasets 2.21.0 | |
| - Tokenizers 0.20.0 | |