Text Generation
PEFT
Safetensors
Transformers
llama
lora
custom_code
text-generation-inference
4-bit precision
bitsandbytes
Instructions to use Nampfiev1995/GoLLIE7B-HIPE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Nampfiev1995/GoLLIE7B-HIPE with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("HiTZ/GoLLIE-7B") model = PeftModel.from_pretrained(base_model, "Nampfiev1995/GoLLIE7B-HIPE") - Transformers
How to use Nampfiev1995/GoLLIE7B-HIPE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Nampfiev1995/GoLLIE7B-HIPE", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Nampfiev1995/GoLLIE7B-HIPE", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("Nampfiev1995/GoLLIE7B-HIPE", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Nampfiev1995/GoLLIE7B-HIPE with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Nampfiev1995/GoLLIE7B-HIPE" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Nampfiev1995/GoLLIE7B-HIPE", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Nampfiev1995/GoLLIE7B-HIPE
- SGLang
How to use Nampfiev1995/GoLLIE7B-HIPE 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 "Nampfiev1995/GoLLIE7B-HIPE" \ --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": "Nampfiev1995/GoLLIE7B-HIPE", "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 "Nampfiev1995/GoLLIE7B-HIPE" \ --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": "Nampfiev1995/GoLLIE7B-HIPE", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Nampfiev1995/GoLLIE7B-HIPE with Docker Model Runner:
docker model run hf.co/Nampfiev1995/GoLLIE7B-HIPE
| { | |
| "best_global_step": null, | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 1.0, | |
| "eval_steps": 100, | |
| "global_step": 21, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.047619047619047616, | |
| "grad_norm": 0.1669921875, | |
| "learning_rate": 0.0, | |
| "loss": 0.2757, | |
| "step": 1 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "eval_hipe2020fr.ner.dev_loss": 0.11246626824140549, | |
| "eval_hipe2020fr.ner.dev_runtime": 12.0267, | |
| "eval_hipe2020fr.ner.dev_samples_per_second": 8.315, | |
| "eval_hipe2020fr.ner.dev_steps_per_second": 1.081, | |
| "step": 21 | |
| } | |
| ], | |
| "logging_steps": 25, | |
| "max_steps": 63, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 3, | |
| "save_steps": 1000, | |
| "stateful_callbacks": { | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": false | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 5.280646810160333e+16, | |
| "train_batch_size": 32, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |