Text Generation
Transformers
Safetensors
Sanskrit
sansar
sanskrit
devanagari
slp1
causal-lm
base-model
custom_code
Eval Results (legacy)
Instructions to use MuseMesh/sansar-700m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MuseMesh/sansar-700m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MuseMesh/sansar-700m", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("MuseMesh/sansar-700m", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MuseMesh/sansar-700m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MuseMesh/sansar-700m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MuseMesh/sansar-700m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MuseMesh/sansar-700m
- SGLang
How to use MuseMesh/sansar-700m 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 "MuseMesh/sansar-700m" \ --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": "MuseMesh/sansar-700m", "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 "MuseMesh/sansar-700m" \ --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": "MuseMesh/sansar-700m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MuseMesh/sansar-700m with Docker Model Runner:
docker model run hf.co/MuseMesh/sansar-700m
Download tokenizer.model from MuseMesh/sansar-700m: direct link, hf CLI and curl.
- Browser
- Download file 116 kB
-
https://huggingface.co/MuseMesh/sansar-700m/resolve/main/tokenizer.model
- Command line
-
hf download hf://MuseMesh/sansar-700m/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/MuseMesh/sansar-700m/resolve/main/tokenizer.model
116 kB
- Xet hash:
- 0be79e53db5348a735ec2f55172aab6d19e680c503ef250eef2adb5e5532f8aa
- Size of remote file:
- 116 kB
- SHA256:
- c9ad72544159bd726fd5c8d4665d58785b7ee4a1de5840f04daa1ca35cb6c382
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