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
Adding Evaluation Results
Browse filesThis is an automated PR created with https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr
The purpose of this PR is to add evaluation results from the Open LLM Leaderboard to your model card.
If you encounter any issues, please report them to https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr/discussions
README.md
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---
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license: other
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license_name: gemma-terms-of-use
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license_link: https://ai.google.dev/gemma/terms
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base_model: google/gemma-2b
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tags:
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- full
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model-index:
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- name: Gemma-2B
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results: []
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datasets:
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- sarvamai/samvaad-hi-v1
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- Transformers 4.39.0.dev0
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- Pytorch 2.0.1+cu118
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- Datasets 2.16.1
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- Tokenizers 0.15.0
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---
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license: other
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tags:
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- full
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datasets:
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- sarvamai/samvaad-hi-v1
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license_name: gemma-terms-of-use
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license_link: https://ai.google.dev/gemma/terms
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base_model: google/gemma-2b
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model-index:
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- name: Gemma-2B
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- Transformers 4.39.0.dev0
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- Pytorch 2.0.1+cu118
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- Datasets 2.16.1
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- Tokenizers 0.15.0
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Tensoic__Gemma-2B-Samvaad)
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| Metric |Value|
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|Avg. |42.55|
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|AI2 Reasoning Challenge (25-Shot)|46.59|
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|HellaSwag (10-Shot) |68.17|
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|MMLU (5-Shot) |33.09|
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|TruthfulQA (0-shot) |39.95|
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|Winogrande (5-shot) |61.64|
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|GSM8k (5-shot) | 5.84|
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