Text Generation
Transformers
Safetensors
German
eva_gpt
gpt
llm
large language model
matelix-ai
conversational
mxfp4
Instructions to use MTSmash/EvaGPT-German-2B-Q11-Pretrained-own-Tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MTSmash/EvaGPT-German-2B-Q11-Pretrained-own-Tokenizer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MTSmash/EvaGPT-German-2B-Q11-Pretrained-own-Tokenizer") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("MTSmash/EvaGPT-German-2B-Q11-Pretrained-own-Tokenizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MTSmash/EvaGPT-German-2B-Q11-Pretrained-own-Tokenizer with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MTSmash/EvaGPT-German-2B-Q11-Pretrained-own-Tokenizer" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MTSmash/EvaGPT-German-2B-Q11-Pretrained-own-Tokenizer", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/MTSmash/EvaGPT-German-2B-Q11-Pretrained-own-Tokenizer
- SGLang
How to use MTSmash/EvaGPT-German-2B-Q11-Pretrained-own-Tokenizer 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 "MTSmash/EvaGPT-German-2B-Q11-Pretrained-own-Tokenizer" \ --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": "MTSmash/EvaGPT-German-2B-Q11-Pretrained-own-Tokenizer", "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 "MTSmash/EvaGPT-German-2B-Q11-Pretrained-own-Tokenizer" \ --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": "MTSmash/EvaGPT-German-2B-Q11-Pretrained-own-Tokenizer", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use MTSmash/EvaGPT-German-2B-Q11-Pretrained-own-Tokenizer with Docker Model Runner:
docker model run hf.co/MTSmash/EvaGPT-German-2B-Q11-Pretrained-own-Tokenizer
| architecture: | |
| backbone_dtype: bfloat16 | |
| gradient_checkpointing: true | |
| intermediate_dropout: 0.0 | |
| pretrained: true | |
| pretrained_weights: /home/mtsmash/Schreibtisch/h2o-llmstudio-eva-gpt/output/user/EvaGPT-German-2B-Q10/checkpoint.pth | |
| augmentation: | |
| neftune_noise_alpha: 0.0 | |
| random_parent_probability: 0.0 | |
| skip_parent_probability: 0.0 | |
| token_mask_probability: 0.0 | |
| dataset: | |
| add_eos_token_to_answer: true | |
| add_eos_token_to_prompt: true | |
| add_eos_token_to_system: true | |
| answer_column: Assistentin | |
| chatbot_author: MaTeLiX AI | |
| chatbot_name: h2oGPT | |
| data_sample: 1.0 | |
| data_sample_choice: | |
| - Train | |
| - Validation | |
| id_column: id | |
| limit_chained_samples: true | |
| mask_prompt_labels: true | |
| mask_prompt_user_text_only: false | |
| only_last_answer: false | |
| parent_id_column: parent_id | |
| personalize: false | |
| prompt_column: | |
| - Kontext | |
| - Benutzer | |
| prompt_column_separator: \n\n | |
| system_column: system | |
| text_answer_separator: <|Assistentin|> | |
| text_prompt_start: <|Benutzer|> | |
| text_system_start: <|System|> | |
| train_dataframe: /home/mtsmash/Schreibtisch/h2o-llmstudio-eva-gpt/data/user/04-07-2026-11/04-07-2026-11.csv | |
| train_text_column: false | |
| validation_dataframe: /home/mtsmash/Schreibtisch/h2o-llmstudio-eva-gpt/data/user/04-07-2026-11/04-07-2026-11.csv | |
| validation_size: 0.1 | |
| validation_strategy: automatic | |
| environment: | |
| compile_model: false | |
| deepspeed_allgather_bucket_size: 1000000 | |
| deepspeed_method: ZeRO2 | |
| deepspeed_reduce_bucket_size: 1000000 | |
| deepspeed_stage3_param_persistence_threshold: 1000000 | |
| deepspeed_stage3_prefetch_bucket_size: 1000000 | |
| find_unused_parameters: false | |
| gpus: | |
| - '0' | |
| - '1' | |
| - '2' | |
| - '3' | |
| huggingface_branch: main | |
| mixed_precision: false | |
| mixed_precision_dtype: bfloat16 | |
| number_of_workers: 8 | |
| seed: -1 | |
| trust_remote_code: false | |
| use_deepspeed: true | |
| experiment_name: EvaGPT-German-2B-Q11.1 | |
| llm_backbone: /home/mtsmash/Schreibtisch/h2o-llmstudio-eva-gpt/output/user/EvaGPT-German-2B-Q10/ | |
| logging: | |
| log_all_ranks: false | |
| log_step_size: absolute | |
| logger: None | |
| neptune_project: '' | |
| wandb_entity: '' | |
| wandb_project: '' | |
| output_directory: /home/mtsmash/Schreibtisch/h2o-llmstudio-eva-gpt/output/user/EvaGPT-German-2B-Q11.1/ | |
| prediction: | |
| batch_size_inference: 0 | |
| do_sample: false | |
| max_length_inference: 2336 | |
| max_time: 0.0 | |
| metric: Perplexity | |
| metric_gpt_model: gpt-3.5-turbo-0301 | |
| metric_gpt_template: general | |
| min_length_inference: 2 | |
| num_beams: 1 | |
| num_history: 4 | |
| repetition_penalty: 1.1 | |
| stop_tokens: '' | |
| temperature: 0.0 | |
| top_k: 40 | |
| top_p: 0.9 | |
| problem_type: text_causal_language_modeling | |
| tokenizer: | |
| add_prompt_answer_tokens: true | |
| long_sample_strategy: Sliding Window | |
| max_length: 2528 | |
| padding_quantile: 1.0 | |
| sliding_window_overlap: 256 | |
| tokenizer_kwargs: '{"use_fast": true, "add_prefix_space": false}' | |
| training: | |
| attention_implementation: sdpa | |
| batch_size: 4 | |
| differential_learning_rate: 1.0e-05 | |
| differential_learning_rate_layers: [] | |
| drop_last_batch: true | |
| epochs: 3 | |
| evaluate_before_training: false | |
| evaluation_epochs: 1.0 | |
| freeze_layers: [] | |
| grad_accumulation: 8 | |
| gradient_clip: 1.0 | |
| learning_rate: 0.0002 | |
| lora: false | |
| lora_alpha: 24 | |
| lora_dropout: 0.05 | |
| lora_r: 16 | |
| lora_target_modules: q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj | |
| lora_unfreeze_layers: [] | |
| loss_function: StableTokenCrossEntropy | |
| min_learning_rate_ratio: 0.0 | |
| optimizer: AdamW | |
| save_checkpoint: last | |
| schedule: LossAwareCosine | |
| train_validation_data: true | |
| use_dora: false | |
| use_rslora: false | |
| warmup_epochs: 0.0 | |
| weight_decay: 0.01 | |