Instructions to use Nisk36/MergeModelAll with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nisk36/MergeModelAll with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Nisk36/MergeModelAll")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Nisk36/MergeModelAll") model = AutoModelForCausalLM.from_pretrained("Nisk36/MergeModelAll", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Nisk36/MergeModelAll with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Nisk36/MergeModelAll" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Nisk36/MergeModelAll", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Nisk36/MergeModelAll
- SGLang
How to use Nisk36/MergeModelAll 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 "Nisk36/MergeModelAll" \ --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": "Nisk36/MergeModelAll", "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 "Nisk36/MergeModelAll" \ --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": "Nisk36/MergeModelAll", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Nisk36/MergeModelAll with Docker Model Runner:
docker model run hf.co/Nisk36/MergeModelAll
| base_model: | |
| - Nisk36/finetuned-lmsys_vicuna-7b-v1.5 | |
| - Nisk36/FT_elyza_ELYZA-japanese-Llama-2-7b-instruct | |
| library_name: transformers | |
| tags: | |
| - mergekit | |
| - merge | |
| # final_model | |
| 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 [linear](https://arxiv.org/abs/2203.05482) merge method. | |
| ### Models Merged | |
| The following models were included in the merge: | |
| * [Nisk36/finetuned-lmsys_vicuna-7b-v1.5](https://huggingface.co/Nisk36/finetuned-lmsys_vicuna-7b-v1.5) | |
| * [Nisk36/FT_elyza_ELYZA-japanese-Llama-2-7b-instruct](https://huggingface.co/Nisk36/FT_elyza_ELYZA-japanese-Llama-2-7b-instruct) | |
| ### Configuration | |
| The following YAML configuration was used to produce this model: | |
| ```yaml | |
| dtype: bfloat16 | |
| merge_method: linear | |
| parameters: | |
| int8_mask: 1.0 | |
| normalize: 1.0 | |
| slices: | |
| - sources: | |
| - layer_range: [0, 4] | |
| model: Nisk36/finetuned-lmsys_vicuna-7b-v1.5 | |
| parameters: | |
| weight: 0.6235769265047518 | |
| - layer_range: [0, 4] | |
| model: Nisk36/FT_elyza_ELYZA-japanese-Llama-2-7b-instruct | |
| parameters: | |
| weight: 0.7274442555681364 | |
| - sources: | |
| - layer_range: [4, 8] | |
| model: Nisk36/finetuned-lmsys_vicuna-7b-v1.5 | |
| parameters: | |
| weight: 0.5271398694239577 | |
| - layer_range: [4, 8] | |
| model: Nisk36/FT_elyza_ELYZA-japanese-Llama-2-7b-instruct | |
| parameters: | |
| weight: 0.3489250438855029 | |
| - sources: | |
| - layer_range: [8, 12] | |
| model: Nisk36/finetuned-lmsys_vicuna-7b-v1.5 | |
| parameters: | |
| weight: 0.15496421762028023 | |
| - layer_range: [8, 12] | |
| model: Nisk36/FT_elyza_ELYZA-japanese-Llama-2-7b-instruct | |
| parameters: | |
| weight: 0.541330668871115 | |
| - sources: | |
| - layer_range: [12, 16] | |
| model: Nisk36/finetuned-lmsys_vicuna-7b-v1.5 | |
| parameters: | |
| weight: 0.5267269624685371 | |
| - layer_range: [12, 16] | |
| model: Nisk36/FT_elyza_ELYZA-japanese-Llama-2-7b-instruct | |
| parameters: | |
| weight: 0.8265113027826562 | |
| - sources: | |
| - layer_range: [16, 20] | |
| model: Nisk36/finetuned-lmsys_vicuna-7b-v1.5 | |
| parameters: | |
| weight: 0.6599861585345389 | |
| - layer_range: [16, 20] | |
| model: Nisk36/FT_elyza_ELYZA-japanese-Llama-2-7b-instruct | |
| parameters: | |
| weight: -0.249060520039947 | |
| - sources: | |
| - layer_range: [20, 24] | |
| model: Nisk36/finetuned-lmsys_vicuna-7b-v1.5 | |
| parameters: | |
| weight: 0.7761318532349375 | |
| - layer_range: [20, 24] | |
| model: Nisk36/FT_elyza_ELYZA-japanese-Llama-2-7b-instruct | |
| parameters: | |
| weight: 0.7040995904551324 | |
| - sources: | |
| - layer_range: [24, 28] | |
| model: Nisk36/finetuned-lmsys_vicuna-7b-v1.5 | |
| parameters: | |
| weight: 0.40152017541360374 | |
| - layer_range: [24, 28] | |
| model: Nisk36/FT_elyza_ELYZA-japanese-Llama-2-7b-instruct | |
| parameters: | |
| weight: 0.767141768059921 | |
| - sources: | |
| - layer_range: [28, 32] | |
| model: Nisk36/finetuned-lmsys_vicuna-7b-v1.5 | |
| parameters: | |
| weight: -0.004536646708608122 | |
| - layer_range: [28, 32] | |
| model: Nisk36/FT_elyza_ELYZA-japanese-Llama-2-7b-instruct | |
| parameters: | |
| weight: 0.8295357241419378 | |
| ``` | |