Instructions to use GenVRadmin/Llamavaad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GenVRadmin/Llamavaad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="GenVRadmin/Llamavaad")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("GenVRadmin/Llamavaad") model = AutoModelForCausalLM.from_pretrained("GenVRadmin/Llamavaad") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use GenVRadmin/Llamavaad with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "GenVRadmin/Llamavaad" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "GenVRadmin/Llamavaad", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/GenVRadmin/Llamavaad
- SGLang
How to use GenVRadmin/Llamavaad 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 "GenVRadmin/Llamavaad" \ --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": "GenVRadmin/Llamavaad", "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 "GenVRadmin/Llamavaad" \ --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": "GenVRadmin/Llamavaad", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use GenVRadmin/Llamavaad with Docker Model Runner:
docker model run hf.co/GenVRadmin/Llamavaad
More information the model, chat template and examples.
Hi there,
Congratulations on open-sourcing such a brilliant model. Hinglish LLMs are definitely something the community can benefit quite a lot from.
Could you please provide more information about the model, chat template, how to use it and some examples in the model card?
I believe this would result in a considerable increase in the adoption of the model.
Lastly, are there plans to train smaller 7B/ 13B scale models as well?
Cheers!
VB
Yes, we have made a demo video for this: https://www.linkedin.com/feed/update/urn:li:activity:7169266339990233088
Chat template is same as Llama-70b: [INST] query[/INST] answer
Stop words are: [INST]
I will add it to model card.
We have made llama-7b (closed source), llama-13b (open source - 3 variants - Eng to Hindi, Hindi to Eng and Hindi chat) and Mixtral(closed source) in the past. Now, working on other models (particularly Deepseek-7b-Maths).
Our previous models were shared on our LinkedIn:
https://www.linkedin.com/feed/update/urn:li:activity:7143255593103810560
https://www.linkedin.com/feed/update/urn:li:activity:7147075414304112640
And can be downloaded from Github links on those posts.