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: 2,414 Bytes

da19444

# Korean LLM 3B parameters — FP8 (B200 TransformerEngine MXFP8)
#
# [아키텍처 근거 — 2026-02-27]
#   - 저스티스리그 제안 기반: d_model=2560, 32L, 32H, 8KV
#   - 파라미터: ~2.39B ("3B급" — Llama-3.2-3B 대비 경량, 한국어 64K vocab 효율)
#   - d_ffn=6912: 2.7×d_model, 16배수 FP8 정렬
#   - GQA 4:1 (32H:8KV) — 추론 효율 + KV cache 절약
#   - head_dim=80 (2560/32) — Flash Attention 효율적
#
# [데이터/학습 설계]
#   - 데이터: korean_train.bin 8.91B tokens
#   - Chinchilla 최적: 2.4B × 20 = 48B tokens
#   - 실제 목표: 60B tokens (6.7 에포크) — 한국어 단일 언어 특성상 추가 학습 유리
#   - max_steps 57000 = 60B tokens / 1,048,576 tok/step
#
# [GPU 메모리 예측 — 8× B200 183GB]
#   - 모델 FP8: 2.4 GB
#   - Optimizer (bf16 master + fp32 mom/var): 23.9 GB
#   - Gradient (bf16): 4.8 GB
#   - Activation (per GPU, bs=8): ~27 GB
#   - 합계: ~58 GB/GPU (31.7% 활용) → 여유 충분
#
# 실행: bash scripts/launch_korean_3b.sh
# 테스트: RUN_NAME=korean_3b_test bash scripts/launch_korean_3b.sh --max_steps 50

model:
  vocab_size: 64000
  d_model: 2560
  n_layers: 32
  n_heads: 32
  n_kv_heads: 8           # GQA 4:1 (K/V 파라미터 75% 절감)
  d_ffn: 6912             # 2.7×d_model, 16배수 (FP8 alignment)
  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:
  # 57k steps × 1,048,576 tok/step = 59.8B tokens ≈ 6.7 에포크
  max_steps: 57000
  batch_size: 4            # per GPU: 4 × 4096 = 16,384 토큰 | VRAM ~130 GB (183GB의 71%)
  grad_accum_steps: 8      # eff_batch: 4 × 8GPU × 8 × 4096 = 1,048,576 tok/step
  lr: 1.5e-4               # 3B 규모: GPT-3 scaling 기준 1B(2e-4) → 3B(1.5e-4)
  weight_decay: 0.1
  warmup_steps: 2000       # 57k steps의 3.5% — 안정적 warmup
  max_grad_norm: 1.0
  log_interval: 10
  save_interval: 1000      # 57k steps 기준 ~57 체크포인트
  eval_interval: 500       # val loss 모니터링
  use_amp: false           # fp8_autocast가 대체
  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