Instructions to use Tensoic/Gemma-2B-Samvaad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tensoic/Gemma-2B-Samvaad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Tensoic/Gemma-2B-Samvaad")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Tensoic/Gemma-2B-Samvaad") model = AutoModelForCausalLM.from_pretrained("Tensoic/Gemma-2B-Samvaad") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Tensoic/Gemma-2B-Samvaad with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Tensoic/Gemma-2B-Samvaad" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Tensoic/Gemma-2B-Samvaad", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Tensoic/Gemma-2B-Samvaad
- SGLang
How to use Tensoic/Gemma-2B-Samvaad 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 "Tensoic/Gemma-2B-Samvaad" \ --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": "Tensoic/Gemma-2B-Samvaad", "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 "Tensoic/Gemma-2B-Samvaad" \ --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": "Tensoic/Gemma-2B-Samvaad", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Tensoic/Gemma-2B-Samvaad with Docker Model Runner:
docker model run hf.co/Tensoic/Gemma-2B-Samvaad
metadata
license: other
tags:
- full
datasets:
- sarvamai/samvaad-hi-v1
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
base_model: google/gemma-2b
model-index:
- name: Gemma-2B
results: []
final_trained
This model is a fine-tuned version of google/gemma-2b on the samvaad-hi-v1 dataset.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1.0
Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 42.55 |
| AI2 Reasoning Challenge (25-Shot) | 46.59 |
| HellaSwag (10-Shot) | 68.17 |
| MMLU (5-Shot) | 33.09 |
| TruthfulQA (0-shot) | 39.95 |
| Winogrande (5-shot) | 61.64 |
| GSM8k (5-shot) | 5.84 |