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: 1,947 Bytes

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# Korean LLM 3B parameters — FP8 (B200 TransformerEngine MXFP8)
#
# [설계 근거 — 2026-02-27]
#   - 아키텍처: LLaMA-3 3B 참고 (d=3072, 28L, 24H, GQA 8:1)
#   - 파라미터: ~3.0B (embedding 포함)
#   - 데이터: korean_train.bin 8.91B tokens → 최소 60B tokens (7 에포크)
#   - Chinchilla optimal: 3B 모델 → 60B tokens, 실용적으로 100B 권장
#   - lr=1.5e-4: LLaMA-3 3B 기준 (1B의 2e-4 대비 낮춤, μP scaling ~1/sqrt(3))
#   - eff_batch=2M tokens: 3B 기준 GPT-3 scaling law 참고
#   - 체크포인트: ~27GB/개, 2000 step 간격 → 최대 ~30개 = 810GB
#   - 예상 학습 시간: 8×B200 FP8 기준 ~72-96시간 (60B tokens)
#
# 실행: bash scripts/launch_3b_pretrain.sh
model:
  vocab_size: 64000
  d_model: 3072
  n_layers: 28
  n_heads: 24
  n_kv_heads: 8          # GQA 3:1 (메모리 효율 + 품질 밸런스)
  d_ffn: 8192            # ~2.67× d_model, 128배수 (FP8 alignment)
  max_seq_len: 4096
  rope_theta: 500000.0
  dropout: 0.0
  bias: false
  use_flash_attn: true
  use_fp8: true

train:
  # Phase 1: 60B tokens (최소) = 57000 steps × 2^20 tok/step
  # Phase 2: 100B tokens (권장) = 95000 steps
  max_steps: 57000
  batch_size: 5          # per GPU: 5 × 4096 = 20,480 토큰 (QKV fusion 후 ~161GB/183GB VRAM, 21GB 여유)
  grad_accum_steps: 8    # eff_batch: 5 × 8GPU × 8 × 4096 = 1,310,720 tok/step (~1.3M)
  lr: 1.5e-4             # LLaMA-3 3B 스케일, Chinchilla 참고
  weight_decay: 0.1
  warmup_steps: 2000     # 57k의 3.5%
  max_grad_norm: 1.0
  log_interval: 10
  save_interval: 2000    # 27GB/체크포인트 → 2000 step 간격 = ~28개 = 756GB
  eval_interval: 500
  use_amp: false
  compile_model: false
  fp8_amax_history_len: 16   # NOTE: MXFP8 format에서는 무시됨 (DelayedScaling 전용)
  fp8_amax_compute_algo: "max"  # NOTE: MXFP8 format에서는 무시됨
  fp8_format: "MXFP8"

tokenizer:
  vocab_size: 64000
  type: sentencepiece_unigram