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 new

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

somebody-to-love commited on Mar 9

Commit

f45e7ff

verified ·

1 Parent(s): 3d85abb

Upload README.md with huggingface_hub

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README.md +68 -3

README.md CHANGED Viewed

@@ -1,3 +1,68 @@
----
-license: mit
----

+---
+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 256개**를 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.