Text Generation
Transformers
Safetensors
mistral
mergekit
Merge
conversational
text-generation-inference
Instructions to use Cartinoe5930/DARE-Merging with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cartinoe5930/DARE-Merging with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Cartinoe5930/DARE-Merging") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Cartinoe5930/DARE-Merging") model = AutoModelForCausalLM.from_pretrained("Cartinoe5930/DARE-Merging", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Cartinoe5930/DARE-Merging with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Cartinoe5930/DARE-Merging" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Cartinoe5930/DARE-Merging", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Cartinoe5930/DARE-Merging
- SGLang
How to use Cartinoe5930/DARE-Merging 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 "Cartinoe5930/DARE-Merging" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Cartinoe5930/DARE-Merging", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "Cartinoe5930/DARE-Merging" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Cartinoe5930/DARE-Merging", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Cartinoe5930/DARE-Merging with Docker Model Runner:
docker model run hf.co/Cartinoe5930/DARE-Merging
File size: 1,499 Bytes
12c34f6 bd50720 12c34f6 bd50720 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | ---
base_model:
- openchat/openchat-3.5-0106
- mistralai/Mistral-7B-Instruct-v0.2
- Open-Orca/Mistral-7B-OpenOrca
- WizardLM/WizardMath-7B-V1.1
tags:
- mergekit
- merge
license: apache-2.0
---
# result
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 [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) as a base.
### Models Merged
The following models were included in the merge:
* [openchat/openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106)
* [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca)
* [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: mistralai/Mistral-7B-Instruct-v0.2
# No parameters necessary for base model
- model: Open-Orca/Mistral-7B-OpenOrca
parameters:
density: 0.5
weight: 0.3
- model: openchat/openchat-3.5-0106
parameters:
density: 0.5
weight: 0.3
- model: WizardLM/WizardMath-7B-V1.1
parameters:
density: 0.5
weight: 0.3
merge_method: dare_ties
base_model: mistralai/Mistral-7B-Instruct-v0.2
parameters:
normalize: true
dtype: float16
``` |