Text Generation
Transformers
PyTorch
llama
Generated from Trainer
conversational
text-generation-inference
Instructions to use illuin-explo/CroissantLLM_ft_translation_correction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use illuin-explo/CroissantLLM_ft_translation_correction with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="illuin-explo/CroissantLLM_ft_translation_correction") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("illuin-explo/CroissantLLM_ft_translation_correction") model = AutoModelForCausalLM.from_pretrained("illuin-explo/CroissantLLM_ft_translation_correction", device_map="auto") 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 Settings
- vLLM
How to use illuin-explo/CroissantLLM_ft_translation_correction with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "illuin-explo/CroissantLLM_ft_translation_correction" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "illuin-explo/CroissantLLM_ft_translation_correction", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/illuin-explo/CroissantLLM_ft_translation_correction
- SGLang
How to use illuin-explo/CroissantLLM_ft_translation_correction 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 "illuin-explo/CroissantLLM_ft_translation_correction" \ --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": "illuin-explo/CroissantLLM_ft_translation_correction", "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 "illuin-explo/CroissantLLM_ft_translation_correction" \ --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": "illuin-explo/CroissantLLM_ft_translation_correction", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use illuin-explo/CroissantLLM_ft_translation_correction with Docker Model Runner:
docker model run hf.co/illuin-explo/CroissantLLM_ft_translation_correction
| license: mit | |
| base_model: croissantllm/CroissantCool-v0.2 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: gpfs/workdir/fayssema/models/out_newtok_dataset1 | |
| 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 | |
| base_model: croissantllm/CroissantCool-v0.2 | |
| model_type: LlamaForCausalLM | |
| tokenizer_type: LlamaTokenizerFast | |
| is_llama_derived_model: true | |
| special_tokens: | |
| bos_token: "<s>" | |
| eos_token: "</s>" | |
| unk_token: "<unk>" | |
| tokens: | |
| - "<|im_start|>" | |
| - "<|im_end|>" | |
| load_in_8bit: false | |
| load_in_4bit: false | |
| strict: false | |
| datasets: | |
| - path: manu/dataset_1 | |
| split: train | |
| type: sharegpt | |
| chat_template: "chatml" | |
| default_system_message: null | |
| dataset_prepared_path: new_pii_2 | |
| val_set_size: 0.05 | |
| output_dir: /gpfs/workdir/fayssema/models/out_newtok_dataset1 | |
| sequence_len: 2048 | |
| sample_packing: false | |
| pad_to_sequence_len: false | |
| adapter: | |
| lora_model_dir: | |
| lora_r: | |
| lora_alpha: | |
| lora_dropout: | |
| lora_target_linear: | |
| lora_fan_in_fan_out: | |
| wandb_project: | |
| wandb_entity: | |
| wandb_watch: | |
| wandb_name: | |
| wandb_log_model: | |
| gradient_accumulation_steps: 2 | |
| micro_batch_size: 16 | |
| num_epochs: 3 | |
| # optimizer: adamw_bnb_8bit | |
| lr_scheduler: cosine | |
| learning_rate: 0.00003 | |
| train_on_inputs: false | |
| group_by_length: false | |
| bf16: auto | |
| fp16: false | |
| tf32: true | |
| gradient_checkpointing: true | |
| early_stopping_patience: | |
| resume_from_checkpoint: | |
| local_rank: | |
| logging_steps: 1 | |
| xformers_attention: | |
| flash_attention: true | |
| flash_attn_cross_entropy: false | |
| flash_attn_rms_norm: true | |
| flash_attn_fuse_qkv: false | |
| flash_attn_fuse_mlp: true | |
| warmup_steps: 100 | |
| evals_per_epoch: 4 | |
| eval_table_size: | |
| saves_per_epoch: 1 | |
| debug: | |
| deepspeed: #deepspeed_configs/zero2.json # multi-gpu only | |
| weight_decay: 0.05 | |
| fsdp: | |
| fsdp_config: | |
| ``` | |
| </details><br> | |
| # gpfs/workdir/fayssema/models/out_newtok_dataset1 | |
| This model is a fine-tuned version of [croissantllm/CroissantCool-v0.2](https://huggingface.co/croissantllm/CroissantCool-v0.2) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.0087 | |
| ## 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: 3e-05 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 2 | |
| - total_train_batch_size: 32 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_steps: 100 | |
| - num_epochs: 3 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 1.0845 | 0.0 | 1 | 0.8684 | | |
| | 0.1841 | 0.25 | 73 | 0.0205 | | |
| | 0.2394 | 0.51 | 146 | 0.0134 | | |
| | 0.1685 | 0.76 | 219 | 0.0128 | | |
| | 0.1385 | 1.01 | 292 | 0.0209 | | |
| | 0.1561 | 1.26 | 365 | 0.0128 | | |
| | 0.1352 | 1.52 | 438 | 0.0090 | | |
| | 0.162 | 1.77 | 511 | 0.0094 | | |
| | 0.0661 | 2.02 | 584 | 0.0085 | | |
| | 0.1344 | 2.27 | 657 | 0.0089 | | |
| | 0.0718 | 2.53 | 730 | 0.0088 | | |
| | 0.0942 | 2.78 | 803 | 0.0087 | | |
| ### Framework versions | |
| - Transformers 4.38.0.dev0 | |
| - Pytorch 2.3.0+cu121 | |
| - Datasets 2.16.1 | |
| - Tokenizers 0.15.0 | |