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

pathcosmos commited on Mar 9

Commit

dc788f2

verified ·

1 Parent(s): ef6210a

docs: add detailed sampling config section with eval grid results

Browse files

Files changed (1) hide show

README.md +49 -0

README.md CHANGED Viewed

@@ -99,6 +99,55 @@ llama.cpp/GGUF 추론 시 줄바꿈(`\n`) 등 미등록 문자로 인한 크래
 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.

 상세: `reports/2026-03-09_GGUF_DEPLOYMENT_AND_EVAL_REPORT.md`, `eval/results/frankenstallm-3b-v2/ollama_benchmark_summary.md`
+## 샘플링 파라미터 (Sampling Config)
+ORPO 평가 그리드 실측 최적값 (`t0.7_rep1.2`) 기준입니다.
+### 권장 파라미터
+| 파라미터 | 값 | 비고 |
+|---------|-----|------|
+| `temperature` | **0.7** | 창의성/일관성 균형점 |
+| `repetition_penalty` (PyTorch) | **1.2** | 반복 억제 |
+| `repeat_penalty` (Ollama) | **1.2** | 동일 값 |
+| `top_p` | **0.9** | nucleus sampling |
+| `top_k` | **50** | |
+| `max_new_tokens` | **512** | |
+| `num_ctx` | **4096** | context window |
+### 평가 결과 (ORPO eval grid)
+| 설정 | 3-gram 반복률 | 4-gram 반복률 | EOS 종료율 | 평균 토큰 수 |
+|------|-------------|-------------|----------|------------|
+| **t0.7 / rep1.2 (권장)** | **0.0%** | **0.0%** | **100%** | 189.2 |
+| t0.8 / rep1.05 (기본) | 4.7% | 2.3% | 100% | 221.4 |
+| greedy (temp=0) | 30.89% | — | 66.67% | — |
+> Ollama Q4_K_M 실측: 3-gram 반복 1.8% (자연 어절 반복), EOS 100% 종료 확인.
+> greedy 대비 반복률 **30.89% → 0%** 해소.
+### Transformers 사용 예시
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+model = AutoModelForCausalLM.from_pretrained("pathcosmos/frankenstallm")
+tokenizer = AutoTokenizer.from_pretrained("pathcosmos/frankenstallm")
+inputs = tokenizer("안녕하세요, 오늘 날씨가", return_tensors="pt")
+outputs = model.generate(
+    **inputs,
+    temperature=0.7,
+    repetition_penalty=1.2,
+    top_p=0.9,
+    top_k=50,
+    max_new_tokens=512,
+    do_sample=True,
+)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 ## 사용
 - **Transformers**: 이 체크포인트를 그대로 `from_pretrained(...)` 로 로드 가능.