Instructions to use seawolf2357/kollm8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use seawolf2357/kollm8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="seawolf2357/kollm8")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("seawolf2357/kollm8") model = AutoModelForCausalLM.from_pretrained("seawolf2357/kollm8") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use seawolf2357/kollm8 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "seawolf2357/kollm8" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "seawolf2357/kollm8", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/seawolf2357/kollm8
- SGLang
How to use seawolf2357/kollm8 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 "seawolf2357/kollm8" \ --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": "seawolf2357/kollm8", "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 "seawolf2357/kollm8" \ --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": "seawolf2357/kollm8", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use seawolf2357/kollm8 with Docker Model Runner:
docker model run hf.co/seawolf2357/kollm8
| base_model: | |
| - MoaData/Myrrh_solar_10.7b_3.0 | |
| - chihoonlee10/T3Q-ko-solar-dpo-v7.0 | |
| library_name: transformers | |
| tags: | |
| - mergekit | |
| - merge | |
| # merge | |
| This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). | |
| ## Merge Details | |
| ### Merge Method | |
| This model was merged using the SLERP merge method. | |
| ### Models Merged | |
| The following models were included in the merge: | |
| * [MoaData/Myrrh_solar_10.7b_3.0](https://huggingface.co/MoaData/Myrrh_solar_10.7b_3.0) | |
| * [chihoonlee10/T3Q-ko-solar-dpo-v7.0](https://huggingface.co/chihoonlee10/T3Q-ko-solar-dpo-v7.0) | |
| ### Configuration | |
| The following YAML configuration was used to produce this model: | |
| ```yaml | |
| slices: | |
| - sources: | |
| - model: chihoonlee10/T3Q-ko-solar-dpo-v7.0 | |
| layer_range: | |
| - 0 | |
| - 32 | |
| - model: MoaData/Myrrh_solar_10.7b_3.0 | |
| layer_range: | |
| - 0 | |
| - 32 | |
| merge_method: slerp | |
| base_model: chihoonlee10/T3Q-ko-solar-dpo-v7.0 | |
| parameters: | |
| t: | |
| - filter: self_attn | |
| value: | |
| - 0 | |
| - 0.5 | |
| - 0.3 | |
| - 0.7 | |
| - 1 | |
| - filter: mlp | |
| value: | |
| - 1 | |
| - 0.5 | |
| - 0.7 | |
| - 0.3 | |
| - 0 | |
| - value: 0.5 | |
| dtype: bfloat16 | |
| ``` | |