Instructions to use LLM-OS-Models/KoHRM-Text-1.4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use LLM-OS-Models/KoHRM-Text-1.4B-GGUF 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 LLM-OS-Models/KoHRM-Text-1.4B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf LLM-OS-Models/KoHRM-Text-1.4B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf LLM-OS-Models/KoHRM-Text-1.4B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf LLM-OS-Models/KoHRM-Text-1.4B-GGUF: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 LLM-OS-Models/KoHRM-Text-1.4B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf LLM-OS-Models/KoHRM-Text-1.4B-GGUF: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 LLM-OS-Models/KoHRM-Text-1.4B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf LLM-OS-Models/KoHRM-Text-1.4B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/LLM-OS-Models/KoHRM-Text-1.4B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use LLM-OS-Models/KoHRM-Text-1.4B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LLM-OS-Models/KoHRM-Text-1.4B-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LLM-OS-Models/KoHRM-Text-1.4B-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LLM-OS-Models/KoHRM-Text-1.4B-GGUF:Q4_K_M
- Ollama
How to use LLM-OS-Models/KoHRM-Text-1.4B-GGUF with Ollama:
ollama run hf.co/LLM-OS-Models/KoHRM-Text-1.4B-GGUF:Q4_K_M
- Unsloth Studio
How to use LLM-OS-Models/KoHRM-Text-1.4B-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for LLM-OS-Models/KoHRM-Text-1.4B-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for LLM-OS-Models/KoHRM-Text-1.4B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for LLM-OS-Models/KoHRM-Text-1.4B-GGUF to start chatting
- Docker Model Runner
How to use LLM-OS-Models/KoHRM-Text-1.4B-GGUF with Docker Model Runner:
docker model run hf.co/LLM-OS-Models/KoHRM-Text-1.4B-GGUF:Q4_K_M
- Lemonade
How to use LLM-OS-Models/KoHRM-Text-1.4B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull LLM-OS-Models/KoHRM-Text-1.4B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.KoHRM-Text-1.4B-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Update GGUF README with H/L cycle override instructions
Browse files
README.md
CHANGED
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@@ -86,6 +86,50 @@ Run on CPU:
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-p $'ํด์ธ์ฃผ์ ํฌ์์์ ์/๋ฌ๋ฌ ํ์จ ๋ณ๋์ด ์ํ ์์ต๋ฅ ์ ๋ฏธ์น๋ ์ํฅ๊ณผ ๋์ ๋ฐฉ์์ ๊ฐ๋จํ ์ค๋ช
ํด ์ฃผ์ธ์.'
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```
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## CPU Generation Tests
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Tested locally on CPU with the patched `llama.cpp` build and the prompt shown above.
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-p $'ํด์ธ์ฃผ์ ํฌ์์์ ์/๋ฌ๋ฌ ํ์จ ๋ณ๋์ด ์ํ ์์ต๋ฅ ์ ๋ฏธ์น๋ ์ํฅ๊ณผ ๋์ ๋ฐฉ์์ ๊ฐ๋จํ ์ค๋ช
ํด ์ฃผ์ธ์.'
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```
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## H/L Cycle Override (์์ ์คํ: H/L ์ฌ์ดํด ์ง์ ์ง์ )
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KoHRM-Text-GGUF stores recurrence settings as GGUF metadata:
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```text
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hrm_text.h_cycles = 2
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hrm_text.l_cycles = 3
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```
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ํ์ฌ ํจ์น๋ llama.cpp์์๋ ์คํ ์ ๋ชจ๋ธ ๋ก๋ฉ ๋จ๊ณ์์ ์๋ ํค๋ฅผ ๋ฉํ๋ฐ์ดํฐ ์ค๋ฒ๋ผ์ด๋ํ ์ ์์ต๋๋ค.
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```bash
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./build-hrm/bin/llama-cli \
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-m ./KoHRM-Text-1.4B-Q8_0.gguf \
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-ngl 0 \
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-t 4 \
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-c 1024 \
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-n 260 \
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--seed 41 \
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--temp 0.45 \
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--top-p 0.9 \
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--repeat-penalty 1.08 \
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--single-turn \
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--simple-io \
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--no-warmup \
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--display-prompt \
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--override-kv hrm_text.h_cycles=int:1 \
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--override-kv hrm_text.l_cycles=int:2 \
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-p $'ํด์ธ์ฃผ์ ํฌ์์์ ์/๋ฌ๋ฌ ํ์จ ๋ณ๋์ด ์ํ ์์ต๋ฅ ์ ๋ฏธ์น๋ ์ํฅ๊ณผ ๋์ ๋ฐฉ์์ ๊ฐ๋จํ ์ค๋ช
ํด ์ฃผ์ธ์.'
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```
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Use case:
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- `h_cycles/l_cycles`๋ฅผ ๋ฎ์ถ๋ฉด ๋์ผ ์กฐ๊ฑด์์ ์๋ต์๋๋ ๋นจ๋ผ์ง๋ ๊ฒฝํฅ์ด ์์ง๋ง ํ์ง ์ ํ๊ฐ ์์ฃผ ์ฆ๊ฐํฉ๋๋ค.
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- `2/3`์ ํ์ฌ ๊ธฐ๋ณธ๊ฐ(๋ฌธ์ ์ ์์ ๋์)์
๋๋ค.
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- `1/2`๋ ์๋ ์ฐ์ ํ
์คํธ๋ก ์ถ์ฒํ๋ฉฐ, ์ค์ ์ ๋ฐ ์ถ๋ก ์์๋ `2/3`์ด ๋ ์์ ์ ์
๋๋ค.
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`--override-kv` uses key format `KEY=TYPE:VALUE`, same as upstream llama.cpp:
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- `hrm_text.h_cycles=int:1`
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- `hrm_text.l_cycles=int:2`
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If you need a persistent configuration (e.g., fixed 1/1 for a workload), export a new GGUF after changing `H_cycles` / `L_cycles` in the source config before `convert_hf_to_gguf.py` conversion. That preserves one set of cycles inside the artifact and avoids runtime override overhead.
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## CPU Generation Tests
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Tested locally on CPU with the patched `llama.cpp` build and the prompt shown above.
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