Text Generation
Transformers
Safetensors
gemma2
trl
sft
Generated from Trainer
conversational
text-generation-inference
Instructions to use Datasmartly/test6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Datasmartly/test6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Datasmartly/test6") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Datasmartly/test6") model = AutoModelForCausalLM.from_pretrained("Datasmartly/test6") 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 Datasmartly/test6 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Datasmartly/test6" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Datasmartly/test6", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Datasmartly/test6
- SGLang
How to use Datasmartly/test6 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 "Datasmartly/test6" \ --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": "Datasmartly/test6", "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 "Datasmartly/test6" \ --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": "Datasmartly/test6", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Datasmartly/test6 with Docker Model Runner:
docker model run hf.co/Datasmartly/test6
| library_name: transformers | |
| license: gemma | |
| base_model: MBZUAI-Paris/Atlas-Chat-9B | |
| tags: | |
| - trl | |
| - sft | |
| - generated_from_trainer | |
| datasets: | |
| - arrow | |
| model-index: | |
| - name: test6 | |
| 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. --> | |
| # test6 | |
| This model is a fine-tuned version of [MBZUAI-Paris/Atlas-Chat-9B](https://huggingface.co/MBZUAI-Paris/Atlas-Chat-9B) on the arrow dataset. | |
| ## 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: 5e-05 | |
| - train_batch_size: 2 | |
| - eval_batch_size: 8 | |
| - seed: 3407 | |
| - distributed_type: multi-GPU | |
| - num_devices: 8 | |
| - gradient_accumulation_steps: 8 | |
| - total_train_batch_size: 128 | |
| - total_eval_batch_size: 64 | |
| - optimizer: Use adafactor and the args are: | |
| No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - num_epochs: 3 | |
| ### Training results | |
| ### Framework versions | |
| - Transformers 4.48.3 | |
| - Pytorch 2.1.0+cu121 | |
| - Datasets 3.4.1 | |
| - Tokenizers 0.21.1 | |