Text Generation
PEFT
Safetensors
GGUF
Transformers
gemma2
lora
sft
trl
unsloth
text-generation-inference
Instructions to use grohitraj/Chem_train with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use grohitraj/Chem_train with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-2-2b-bnb-4bit") model = PeftModel.from_pretrained(base_model, "grohitraj/Chem_train") - Transformers
How to use grohitraj/Chem_train with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="grohitraj/Chem_train")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("grohitraj/Chem_train") model = AutoModelForCausalLM.from_pretrained("grohitraj/Chem_train", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use grohitraj/Chem_train 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 grohitraj/Chem_train:F16 # Run inference directly in the terminal: llama cli -hf grohitraj/Chem_train:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf grohitraj/Chem_train:F16 # Run inference directly in the terminal: llama cli -hf grohitraj/Chem_train:F16
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 grohitraj/Chem_train:F16 # Run inference directly in the terminal: ./llama-cli -hf grohitraj/Chem_train:F16
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 grohitraj/Chem_train:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf grohitraj/Chem_train:F16
Use Docker
docker model run hf.co/grohitraj/Chem_train:F16
- LM Studio
- Jan
- vLLM
How to use grohitraj/Chem_train with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "grohitraj/Chem_train" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "grohitraj/Chem_train", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/grohitraj/Chem_train:F16
- SGLang
How to use grohitraj/Chem_train 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 "grohitraj/Chem_train" \ --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": "grohitraj/Chem_train", "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 "grohitraj/Chem_train" \ --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": "grohitraj/Chem_train", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use grohitraj/Chem_train with Ollama:
ollama run hf.co/grohitraj/Chem_train:F16
- Unsloth Desktop
- Docker Model Runner
How to use grohitraj/Chem_train with Docker Model Runner:
docker model run hf.co/grohitraj/Chem_train:F16
- Lemonade
How to use grohitraj/Chem_train with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull grohitraj/Chem_train:F16
Run and chat with the model
lemonade run user.Chem_train-F16
List all available models
lemonade list
- Atomic Chat
Download training_args.bin from grohitraj/Chem_train: direct link, hf CLI and curl.
- Browser
- Download file 5.75 kB
-
https://huggingface.co/grohitraj/Chem_train/resolve/main/training_args.bin
- Command line
-
hf download hf://grohitraj/Chem_train/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/grohitraj/Chem_train/resolve/main/training_args.bin
5.75 kB
- Xet hash:
- a48ba88259718bc2ec4a1d2c739f15b114685a2a76f04787bb8bf7efa61e604c
- Size of remote file:
- 5.75 kB
- SHA256:
- 915160026513953bee0b02143586b4ac9907db184ecf54d11555af6fb5aed220
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.