Text Generation
PEFT
Safetensors
GGUF
English
bhagavad-gita
bhagavad-geeta
devotional
spirituality
companion
lora
unsloth
saarthi
conversational
Instructions to use simkeyur/Saarthi-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use simkeyur/Saarthi-4B with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-4B") model = PeftModel.from_pretrained(base_model, "simkeyur/Saarthi-4B") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use simkeyur/Saarthi-4B with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf simkeyur/Saarthi-4B:Q4_K_M # Run inference directly in the terminal: llama cli -hf simkeyur/Saarthi-4B:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf simkeyur/Saarthi-4B:Q4_K_M # Run inference directly in the terminal: llama cli -hf simkeyur/Saarthi-4B:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf simkeyur/Saarthi-4B:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf simkeyur/Saarthi-4B:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf simkeyur/Saarthi-4B:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf simkeyur/Saarthi-4B:Q4_K_M
Use Docker
docker model run hf.co/simkeyur/Saarthi-4B:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use simkeyur/Saarthi-4B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "simkeyur/Saarthi-4B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "simkeyur/Saarthi-4B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/simkeyur/Saarthi-4B:Q4_K_M
- Ollama
How to use simkeyur/Saarthi-4B with Ollama:
ollama run hf.co/simkeyur/Saarthi-4B:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use simkeyur/Saarthi-4B with Docker Model Runner:
docker model run hf.co/simkeyur/Saarthi-4B:Q4_K_M
- Lemonade
How to use simkeyur/Saarthi-4B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull simkeyur/Saarthi-4B:Q4_K_M
Run and chat with the model
lemonade run user.Saarthi-4B-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download processor_config.json from simkeyur/Saarthi-4B: direct link, hf CLI and curl.
- Browser
- Download file 1.19 kB
-
https://huggingface.co/simkeyur/Saarthi-4B/resolve/main/processor_config.json
- Command line
-
hf download hf://simkeyur/Saarthi-4B/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/simkeyur/Saarthi-4B/resolve/main/processor_config.json
1.19 kB
| { | |
| "image_processor": { | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "Qwen2VLImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "merge_size": 2, | |
| "patch_size": 16, | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "longest_edge": 16777216, | |
| "shortest_edge": 65536 | |
| }, | |
| "temporal_patch_size": 2 | |
| }, | |
| "processor_class": "Qwen3VLProcessor", | |
| "video_processor": { | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "do_sample_frames": true, | |
| "fps": 2, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "max_frames": 768, | |
| "merge_size": 2, | |
| "min_frames": 4, | |
| "patch_size": 16, | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "return_metadata": false, | |
| "size": { | |
| "longest_edge": 25165824, | |
| "shortest_edge": 4096 | |
| }, | |
| "temporal_patch_size": 2, | |
| "video_processor_type": "Qwen3VLVideoProcessor" | |
| } | |
| } | |