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", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use pathcosmos/frankenstallm 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 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 serve -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 Desktop
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

Atomic Chat

frankenstallm / README.md

pathcosmos

Upload README.md with huggingface_hub (#1)

c6ba436 6 months ago

2.38 kB

language:
  - ko
license: other
tags:
  - llm
  - korean
  - orpo
  - gguf

FRANKENSTALLM 3B v2 (Byte-Fallback Fixed)

한국어 중심 FRANKENSTALLM 3B ORPO 파인튜닝 체크포인트에 byte-fallback 토큰 256개를 추가한 버전입니다.
llama.cpp/GGUF 추론 시 줄바꿈(\n) 등 미등록 문자로 인한 크래시를 방지하기 위해 사용합니다.

모델 상세

항목	값
Architecture	LlamaForCausalLM
Params	~3B
Hidden size	2048
Layers	24
Attention heads	16
KV heads	4
Max position	4096
Vocab size	64,256 (64,000 + 256 byte-fallback)
Training	ORPO (SFT → ORPO)

변경 사항 (v2)

토크나이저: byte_fallback=True, <0x00>~`<0xFF>` 256개 토큰 추가
임베딩: 64,000 → 64,256 리사이즈, 새 토큰 초기화
GGUF 변환·Ollama 배포 시 뉴라인 포함 입력 정상 처리 확인

ORPO 평가 요약 (동일 체크포인트 기준)

평가 일시: 2026-03-09
Preference Accuracy: 76.02%
Reward Margin: 0.6100
Eval Loss: 1.7910 → 1.6250
KoBEST (0-shot) 평균: 52.75%
생성 품질: Greedy 3-gram 반복률 30.89%, EOS 종료율 66.67%
PPL Forgetting: 최대 4.1% (기준 <15%)
종합: 7/10 차원 통과, 정량 스코어 63.7/100

상세: 프로젝트 내 reports/2026-03-09_ORPO_EVALUATION_REPORT.md 참고.

Ollama 배포 벤치마크 (Q4_K_M, 2026-03-09)

모델명: frankenstallm-3b-v2
테스트 수: 35 (자동 20 + 수동 15)
자동 채점 평균: 46.7
카테고리: korean_nlu 100.0, reasoning 50.0, knowledge 75.0, instruction_following 66.7, code 0.0, safety 10.0, repetition_resistance 2.2 등
지연: Avg TTFT 16.7 ms, Avg TPS 142.5

상세: reports/2026-03-09_GGUF_DEPLOYMENT_AND_EVAL_REPORT.md, eval/results/frankenstallm-3b-v2/ollama_benchmark_summary.md

사용

Transformers: 이 체크포인트를 그대로 from_pretrained(...) 로 로드 가능.
GGUF: scripts/fix_tokenizer_byte_fallback.py 적용 후 convert_hf_to_gguf.py → llama-quantize 로 변환한 v2 파이프라인 사용 권장.
이미 변환된 Q4_K_M GGUF는 Ollama에서 frankenstallm-3b-v2 로 배포 가능.

라이선스

프로젝트(FRANKENSTALLM) 라이선스에 따릅니다.