Instructions to use LLM-OS-Models/KoHRM-Text-1.4B-raw-checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LLM-OS-Models/KoHRM-Text-1.4B-raw-checkpoints 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-raw-checkpoints")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("LLM-OS-Models/KoHRM-Text-1.4B-raw-checkpoints", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use LLM-OS-Models/KoHRM-Text-1.4B-raw-checkpoints 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-raw-checkpoints" # 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-raw-checkpoints", "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-raw-checkpoints
- SGLang
How to use LLM-OS-Models/KoHRM-Text-1.4B-raw-checkpoints 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-raw-checkpoints" \ --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-raw-checkpoints", "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-raw-checkpoints" \ --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-raw-checkpoints", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LLM-OS-Models/KoHRM-Text-1.4B-raw-checkpoints with Docker Model Runner:
docker model run hf.co/LLM-OS-Models/KoHRM-Text-1.4B-raw-checkpoints
Add files using upload-large-folder tool
Browse files- .gitattributes +9 -0
- stage1d-hrm-fastcap-repeat3-step730000/all_config.yaml +46 -0
- stage1d-hrm-fastcap-repeat3-step730000/carry_step_730000.0.pt +3 -0
- stage1d-hrm-fastcap-repeat3-step730000/carry_step_730000.1.pt +3 -0
- stage1d-hrm-fastcap-repeat3-step730000/carry_step_730000.2.pt +3 -0
- stage1d-hrm-fastcap-repeat3-step730000/carry_step_730000.3.pt +3 -0
- stage1d-hrm-fastcap-repeat3-step730000/carry_step_730000.4.pt +3 -0
- stage1d-hrm-fastcap-repeat3-step730000/carry_step_730000.5.pt +3 -0
- stage1d-hrm-fastcap-repeat3-step730000/carry_step_730000.6.pt +3 -0
- stage1d-hrm-fastcap-repeat3-step730000/carry_step_730000.7.pt +3 -0
- stage1d-hrm-fastcap-repeat3-step730000/fsdp2_step_730000/.metadata +3 -0
- stage1d-hrm-fastcap-repeat3-step730000/fsdp2_step_730000/__0_0.distcp +3 -0
- stage1d-hrm-fastcap-repeat3-step730000/fsdp2_step_730000/__1_0.distcp +3 -0
- stage1d-hrm-fastcap-repeat3-step730000/fsdp2_step_730000/__2_0.distcp +3 -0
- stage1d-hrm-fastcap-repeat3-step730000/fsdp2_step_730000/__3_0.distcp +3 -0
- stage1d-hrm-fastcap-repeat3-step730000/fsdp2_step_730000/__4_0.distcp +3 -0
- stage1d-hrm-fastcap-repeat3-step730000/fsdp2_step_730000/__5_0.distcp +3 -0
- stage1d-hrm-fastcap-repeat3-step730000/fsdp2_step_730000/__6_0.distcp +3 -0
- stage1d-hrm-fastcap-repeat3-step730000/fsdp2_step_730000/__7_0.distcp +3 -0
- stage1d-hrm-fastcap-repeat3-step730000/latest_checkpoint.txt +1 -0
- stage1d-hrm-fastcap-repeat3-step730000/step_730000_info.json +8 -0
- stage1d-hrm-fastcap-repeat3-step730000/train_metadata.yaml +13 -0
- stage1d-hrm-fastcap-repeat3-step730000/upload_manifest.json +1 -0
.gitattributes
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stage1d-hrm-fastcap-repeat3-step730000/all_config.yaml
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arch:
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H_cycles: 2
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H_override: {}
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L_cycles: 3
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bp_max_steps: 5
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bp_warmup_ratio: 0.2
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expansion: 4
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half_layers: true
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head: lm_head@LMHead
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hidden_size: 1536
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init_type: lecun_normal
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n_layers: 32
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name: baselines.hrm_nocarry_bp_warmup@HierarchicalReasoningModel
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checkpoint_path: /home/work/.data/hrm_text_checkpoints/KoHRM-Text-1.4B-stage1d-hrm-fastcap-repeat3-gbs180
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checkpoint_step_interval: 10000
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data:
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path: /home/work/.data/hrm_text_prepared/koterm_hrm_cleaned_fastcap_stage1_v1
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target_only: true
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ema: 0.9999
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lr_warmup_steps: 2000
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project_name: KoHRM-Text
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resume_from: /home/work/.data/hrm_text_checkpoints/KoHRM-Text-1.4B-stage4c-korean-tool-finance-repeat2-gbs180
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run_name: KoHRM-Text-1.4B-stage1d-hrm-fastcap-repeat3
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seed: 0
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step_730000
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stage1d-hrm-fastcap-repeat3-step730000/step_730000_info.json
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{
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"global_step": 730000,
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"skip_batches_hint": 27044,
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"data_path": "/home/work/.data/hrm_text_prepared/koterm_hrm_cleaned_fastcap_stage1_v1",
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"global_batch_size": 180224
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}
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stage1d-hrm-fastcap-repeat3-step730000/train_metadata.yaml
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max_seq_len: 4096
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| 2 |
+
tokenizer_info:
|
| 3 |
+
boq: <|im_start|>
|
| 4 |
+
condition_mapping:
|
| 5 |
+
cot: <|object_ref_end|>
|
| 6 |
+
direct: <|object_ref_start|>
|
| 7 |
+
noisy: <|quad_start|>
|
| 8 |
+
synth: <|quad_end|>
|
| 9 |
+
eoa: <|box_end|>
|
| 10 |
+
eoq: <|im_end|>
|
| 11 |
+
tokenizer_path: /home/work/.data/huggingface/trained_tokenizers/hrm-ko-terminal-131k-v1/tokenizer.json
|
| 12 |
+
total_length: 14554291763
|
| 13 |
+
vocab_size: 131072
|
stage1d-hrm-fastcap-repeat3-step730000/upload_manifest.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
stage1d-hrm-fastcap-repeat3 step_730000 raw resume checkpoint
|