Instructions to use pathcosmos/frankenstallm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pathcosmos/frankenstallm with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="pathcosmos/frankenstallm")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pathcosmos/frankenstallm")
model = AutoModelForCausalLM.from_pretrained("pathcosmos/frankenstallm")

llama-cpp-python

How to use pathcosmos/frankenstallm with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="pathcosmos/frankenstallm",
	filename="gguf/frankenstallm-3b-Q4_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use pathcosmos/frankenstallm with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf pathcosmos/frankenstallm:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf pathcosmos/frankenstallm:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf pathcosmos/frankenstallm:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf pathcosmos/frankenstallm: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 pathcosmos/frankenstallm:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf pathcosmos/frankenstallm: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 pathcosmos/frankenstallm:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pathcosmos/frankenstallm:Q4_K_M

Use Docker

docker model run hf.co/pathcosmos/frankenstallm:Q4_K_M

LM Studio
Jan

vLLM

How to use pathcosmos/frankenstallm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pathcosmos/frankenstallm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pathcosmos/frankenstallm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pathcosmos/frankenstallm:Q4_K_M

SGLang

How to use pathcosmos/frankenstallm 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 "pathcosmos/frankenstallm" \
    --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": "pathcosmos/frankenstallm",
		"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 "pathcosmos/frankenstallm" \
        --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": "pathcosmos/frankenstallm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use pathcosmos/frankenstallm with Ollama:
```
ollama run hf.co/pathcosmos/frankenstallm:Q4_K_M
```

Unsloth Studio

How to use pathcosmos/frankenstallm with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for pathcosmos/frankenstallm to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for pathcosmos/frankenstallm to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for pathcosmos/frankenstallm to start chatting

Docker Model Runner
How to use pathcosmos/frankenstallm with Docker Model Runner:
```
docker model run hf.co/pathcosmos/frankenstallm:Q4_K_M
```

Lemonade

How to use pathcosmos/frankenstallm with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pathcosmos/frankenstallm:Q4_K_M

Run and chat with the model

lemonade run user.frankenstallm-Q4_K_M

List all available models

lemonade list

frankenstallm / source /CLAUDE.md

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CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

작업 원칙 — 팀플레이

병렬 처리 가능한 작업은 항상 서브 에이전트로 분배한다.

복잡한 코드 작성 / 설계 판단 → model: sonnet
빠른 탐색 · 조회 · 간단한 파일 작성 → model: haiku
에이전트 완료 후 결과 회수; 필요 시 resume 으로 재호출
예: 모델 구현(sonnet) + 데이터 스크립트(sonnet) + 설정 파일(haiku) 동시 실행

프로젝트 목적

소규모 LLM(Large Language Model) 실험 프로젝트. 8× NVIDIA B200 GPU 환경에서 LLM 사전학습(pretraining) 또는 파인튜닝(fine-tuning) 을 직접 구현하고 실험한다.

하드웨어 환경

항목	사양
GPU	8× NVIDIA B200 (183 GB VRAM each, ~1.47 TB total)
RAM	2.2 TB
CUDA	13.0
Storage (작업)	`/PROJECT/0325120031_A/ghong/taketimes/` → 3.5 TB, 여유 2.2 TB
Storage (홈)	`/home/ghong` → 5 GB (소규모 코드만 저장)

주의: 체크포인트, 데이터셋 등 대용량 파일은 반드시 /PROJECT/0325120031_A/ghong/taketimes/llm-bang/ 하위에 저장할 것. 홈 디렉토리(/home/ghong) 용량 초과 주의.

사전 설치된 라이브러리

torch          2.10.0a0+b4e4ee81d3.nv25.12   # NV 커스텀 빌드 (B200 최적화)
flash_attn     2.7.4.post1+25.12              # FlashAttention-2 사용 가능
datasets       4.4.1
tokenizers     0.22.1
huggingface_hub 1.2.3

경고: PyTorch는 NVIDIA 커스텀 빌드(nv25.12)가 설치됨. pip install torch 로 재설치하면 B200 최적화가 깨질 수 있음 — PyTorch 재설치 금지.

추가 설치 필요 라이브러리

pip install transformers accelerate peft trl deepspeed bitsandbytes sentencepiece wandb

권장 프로젝트 구조

llm-bang/
├── CLAUDE.md
├── data/               # 학습 데이터 (원본 텍스트, 전처리 완료본)
├── tokenizer/          # 토크나이저 학습·저장
├── model/              # 모델 아키텍처 정의 (nn.Module)
├── train/              # 학습 스크립트 (단일 GPU / DDP / FSDP)
├── eval/               # 평가 스크립트 (perplexity, downstream task)
├── configs/            # YAML/JSON 학습 설정 파일
└── checkpoints/        # 모델 체크포인트 (대용량)

멀티-GPU 학습 실행 패턴

# torchrun (DDP) — 8 GPU
torchrun --nproc_per_node=8 train/pretrain.py --config configs/small_lm.yaml

# 단일 GPU 테스트
python train/pretrain.py --config configs/small_lm.yaml --device cuda:0

# FSDP (모델 샤딩, 대형 모델)
torchrun --nproc_per_node=8 train/pretrain.py --config configs/large_lm.yaml --strategy fsdp

모델 규모 가이드 (하드웨어 기준)

모델 크기	추천 전략	최소 GPU 수
~1B param	DDP, bf16	1 GPU
~7B param	DDP 또는 FSDP, bf16	2–4 GPU
~13B param	FSDP, bf16/fp8	4 GPU
~70B param	FSDP + ZeRO-3, bf16/fp8	8 GPU

B200은 FP8 네이티브 지원 → 학습 시 torch.float8_e4m3fn 활용 가능.

참고 (이전 프로젝트)

/PROJECT/0325120031_A/ghong/taketimes/_deprecated/work/ — 2CRM 두께 실측값 예측(LightGBM, ClickHouse) 프로젝트. 도메인 데이터(공장 센서, 코일 그레이드) 필요 시 참고.