How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ksjpswaroop/energylm-sft# Run inference directly in the terminal:
llama cli -hf ksjpswaroop/energylm-sftUse 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 ksjpswaroop/energylm-sft# Run inference directly in the terminal:
./llama-cli -hf ksjpswaroop/energylm-sftBuild 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 ksjpswaroop/energylm-sft# Run inference directly in the terminal:
./build/bin/llama-cli -hf ksjpswaroop/energylm-sftUse Docker
docker model run hf.co/ksjpswaroop/energylm-sftQuick Links
EnergyLM-SFT
EnergyLM 3B fine-tuned with supervised fine-tuning (SFT) on identity and energy-domain data.
Model
- Architecture: EnergyLM (3B params, 36 layers, 2048 hidden, GQA)
- Base: EOS-Base pretrained on FineWeb-edu
- Fine-tuning: SFT on Safire/EnergyLM identity and conversational data
Usage
HuggingFace (Transformers)
Install the EnergyLM package and load:
# Requires: pip install transformers
# Clone this repo for model registration
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("ksjpswaroop/energylm-sft")
tokenizer = AutoTokenizer.from_pretrained("ksjpswaroop/energylm-sft")
llama.cpp / GGUF
Download the GGUF file (if available) and run with llama.cpp or llama-cpp-python:
pip install llama-cpp-python
python -c "
from llama_cpp import Llama
llm = Llama(model_path='energylm-sft.gguf', n_ctx=4096)
prompt = '<|user|>\nWho are you?\n<|assistant|>\n'
out = llm(prompt, max_tokens=256, stop=['<|end_of_text|>', '<|user|>'])
print(out['choices'][0]['text'])
"
Identity
This model identifies as EOS-chat / EnergyLM, developed by Safire's AI team, with a focus on energy sector applications.
License
Apache 2.0
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Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf ksjpswaroop/energylm-sft# Run inference directly in the terminal: llama cli -hf ksjpswaroop/energylm-sft