Instructions to use LLM-OS-Models/KoHRM-Text-1.4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLM-OS-Models/KoHRM-Text-1.4B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="LLM-OS-Models/KoHRM-Text-1.4B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LLM-OS-Models/KoHRM-Text-1.4B")
model = AutoModelForCausalLM.from_pretrained("LLM-OS-Models/KoHRM-Text-1.4B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LLM-OS-Models/KoHRM-Text-1.4B 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"
# 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",
		"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

SGLang

How to use LLM-OS-Models/KoHRM-Text-1.4B 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 "LLM-OS-Models/KoHRM-Text-1.4B" \
    --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": "LLM-OS-Models/KoHRM-Text-1.4B",
		"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 "LLM-OS-Models/KoHRM-Text-1.4B" \
        --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": "LLM-OS-Models/KoHRM-Text-1.4B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LLM-OS-Models/KoHRM-Text-1.4B with Docker Model Runner:
```
docker model run hf.co/LLM-OS-Models/KoHRM-Text-1.4B
```

gyung commited on about 3 hours ago

Commit

7ef9fc1

verified ·

1 Parent(s): fed7e11

Fix formatting token rendering in model card

Browse files

Files changed (1) hide show

README.md +18 -18

README.md CHANGED Viewed

@@ -109,15 +109,15 @@ The tokenizer was trained for Korean, English, code, shell/terminal text, and JS
 Formatting tokens:
-| Token | Meaning |
-|---|---|
-| `<|im_start|>` | instruction start |
-| `<|im_end|>` | instruction end |
-| `<|box_end|>` | response/end marker |
-| `<|object_ref_start|>` | direct condition |
-| `<|object_ref_end|>` | chain-of-thought style condition |
-| `<|quad_start|>` | noisy condition |
-| `<|quad_end|>` | synthetic condition |
 Prompt format used by the project-side inference code:
@@ -380,15 +380,15 @@ This work builds on HRM-Text:
 포맷 token:
-| Token | 의미 |
-|---|---|
-| `<|im_start|>` | instruction 시작 |
-| `<|im_end|>` | instruction 종료 |
-| `<|box_end|>` | response/end marker |
-| `<|object_ref_start|>` | direct condition |
-| `<|object_ref_end|>` | chain-of-thought style condition |
-| `<|quad_start|>` | noisy condition |
-| `<|quad_end|>` | synthetic condition |
 프로젝트 내부 inference code가 쓰는 prompt 형식:

 Formatting tokens:
+```text
+<|im_start|>         instruction start
+<|im_end|>           instruction end
+<|box_end|>          response/end marker
+<|object_ref_start|> direct condition
+<|object_ref_end|>   chain-of-thought style condition
+<|quad_start|>       noisy condition
+<|quad_end|>         synthetic condition
+```
 Prompt format used by the project-side inference code:
 포맷 token:
+```text
+<|im_start|>         instruction 시작
+<|im_end|>           instruction 종료
+<|box_end|>          response/end marker
+<|object_ref_start|> direct condition
+<|object_ref_end|>   chain-of-thought style condition
+<|quad_start|>       noisy condition
+<|quad_end|>         synthetic condition
+```
 프로젝트 내부 inference code가 쓰는 prompt 형식: