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

File size: 2,116 Bytes

da19444

# Korean LLM 1B parameters — FP8 변형 (B200 TransformerEngine 네이티브)
#
# [최적화 근거 — 2026-02-25]
#   - 데이터: korean_train.bin 8.91B tokens
#   - max_steps 34000 = 4 에포크 (Muennighoff 2023: 4에포크 초과 시 val loss 상승)
#     * 기존 200k steps = 23.5 에포크 → 오버피팅 위험, compute 낭비
#   - lr=2e-4: GPT-3 1.3B 기준과 정확히 일치 (변경 없음)
#   - eff_batch=1.05M: GPT-3 1.3B 기준과 일치 (변경 없음)
#   - warmup 2000 = 34k의 5.9% (기존 4000 = 11.8%로 과도했음)
#   - save/eval 간격 단축: 34k steps 기준 더 촘촘한 체크포인트 필요
#   - compile_model: false (TE 2.10 graph break 위험, 안정성 우선)
#
# 실행: bash scripts/launch_korean_1b.sh
model:
  vocab_size: 64000
  d_model: 2048
  n_layers: 24
  n_heads: 16
  n_kv_heads: 4          # GQA 4:1 (K/V 파라미터 75% 절감)
  d_ffn: 5472            # 16배수 (FP8 alignment 충족)
  max_seq_len: 4096
  rope_theta: 500000.0
  dropout: 0.0
  bias: false
  use_flash_attn: true
  use_fp8: true          # TransformerEngine MXFP8BlockScaling (B200 네이티브)

train:
  # 34k steps × 1,048,576 tok/step = 35.6B tokens = 4 에포크 (8.91B 데이터 기준)
  max_steps: 34000
  batch_size: 8          # per GPU: 8 × 4096 = 32,768 토큰 | VRAM 30.8% 사용 (192GB)
  grad_accum_steps: 4    # eff_batch: 8 × 8GPU × 4 × 4096 = 1,048,576 tok/step
  lr: 2.0e-4             # GPT-3 1.3B 기준 최적값과 정확히 일치
  weight_decay: 0.1
  warmup_steps: 2000     # 34k steps의 5.9% — 기존 4000은 11.8%로 과도
  max_grad_norm: 1.0
  log_interval: 10
  save_interval: 500     # 34k steps 기준 ~70 체크포인트 (기존 1000은 너무 듬성)
  eval_interval: 200     # val loss 조기 이상 감지용
  use_amp: false         # fp8_autocast가 대체 (torch.autocast 불필요)
  compile_model: false   # TE 2.10 + DDP graph break 위험
  fp8_amax_history_len: 16
  fp8_amax_compute_algo: "max"
  fp8_format: "MXFP8"   # B200 Blackwell 네이티브 블록 스케일링

tokenizer:
  vocab_size: 64000
  type: sentencepiece_unigram