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

ef6210a

verified ·

1 Parent(s): d135462

docs: add training hardware specs (8x B200, EPYC 9365, software stack)

Browse files

Files changed (1) hide show

README.md +57 -2

README.md CHANGED Viewed

@@ -34,6 +34,48 @@ llama.cpp/GGUF 추론 시 줄바꿈(`\n`) 등 미등록 문자로 인한 크래
 - 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
@@ -60,8 +102,21 @@ llama.cpp/GGUF 추론 시 줄바꿈(`\n`) 등 미등록 문자로 인한 크래
 ## 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

 - 임베딩: 64,000 → 64,256 리사이즈, 새 토큰 초기화
 - GGUF 변환·Ollama 배포 시 뉴라인 포함 입력 정상 처리 확인
+## 학습 환경 (Training Hardware)
+### GPU
+| 항목 | 사양 |
+|------|------|
+| **GPU** | 8× NVIDIA B200 |
+| **VRAM (per GPU)** | 183 GB HBM3e |
+| **Total VRAM** | ~1,466 GB (~1.47 TB) |
+| **FP8 Tensor Core** | 2,250 TFLOPS/GPU (총 18,000 TFLOPS) |
+| **BF16 Tensor Core** | 1,125 TFLOPS/GPU |
+| **HBM3e Bandwidth** | ~7.67 TB/s per GPU |
+| **Interconnect** | NVLink 5.0 (NV18, 900 GB/s bidirectional) |
+| **Topology** | NVSwitch — 모든 GPU↔GPU 단일 홉 all-to-all mesh |
+| **SMs per GPU** | 148 |
+| **L2 Cache per GPU** | 126.5 MB |
+### CPU & Memory
+| 항목 | 사양 |
+|------|------|
+| **CPU** | 2× AMD EPYC 9365 (Turin / Zen 5) |
+| **Physical Cores** | 72 (36코어 × 2소켓) |
+| **L3 Cache** | 384 MB (12 CCX × 32 MB) |
+| **System RAM** | 2.21 TB DDR5 (NUMA 2노드 × ~1.1 TB) |
+| **GPU↔NUMA 매핑** | GPU 0–3 → NUMA node 0 / GPU 4–7 → NUMA node 1 |
+### 소프트웨어 스택
+| 패키지 | 버전 |
+|--------|------|
+| **CUDA** | 13.1 |
+| **Driver** | 580.95.05 |
+| **PyTorch** | 2.10.0a0+b4e4ee81d3 (NVIDIA nv25.12 커스텀 빌드, B200 최적화) |
+| **Transformer Engine** | 2.10.0 |
+| **FlashAttention** | 2.7.4.post1+25.12 |
+| **NCCL** | 2.28.9 |
+| **Triton** | 3.5.1 |
+| **TRL** | ORPO fine-tuning |
+> **참고**: PyTorch는 NVIDIA B200 최적화 커스텀 빌드(`nv25.12`)를 사용합니다. FP8 네이티브 연산(`torch.float8_e4m3fn`) 지원 환경입니다.
 ## ORPO 평가 요약 (동일 체크포인트 기준)
 - **평가 일시**: 2026-03-09
 ## 사용
 - **Transformers**: 이 체크포인트를 그대로 `from_pretrained(...)` 로 로드 가능.
+- **GGUF / Ollama**:
+  ```bash
+  # Q4_K_M (권장, 757MB)
+  ollama create frankenstallm-3b-v2:Q4_K_M -f gguf/Modelfile.3b-v2-Q4_K_M
+  ollama run frankenstallm-3b-v2:Q4_K_M
+  # Q8_0 (고품질, 1.2GB)
+  ollama create frankenstallm-3b-v2:Q8_0 -f gguf/Modelfile.3b-v2-Q8_0
+  ollama run frankenstallm-3b-v2:Q8_0
+  # f16 (최고품질, 2.3GB)
+  ollama create frankenstallm-3b-v2:f16 -f gguf/Modelfile.3b-v2-f16
+  ollama run frankenstallm-3b-v2:f16
+  ```
+  Modelfile에 검증된 샘플링 파라미터(`temperature=0.7, repeat_penalty=1.2`)가 포함되어 있습니다.
 ## 라이선스