Instructions to use TitleOS/ExperimentTwo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TitleOS/ExperimentTwo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TitleOS/ExperimentTwo")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TitleOS/ExperimentTwo") model = AutoModelForCausalLM.from_pretrained("TitleOS/ExperimentTwo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TitleOS/ExperimentTwo with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TitleOS/ExperimentTwo" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TitleOS/ExperimentTwo", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TitleOS/ExperimentTwo
- SGLang
How to use TitleOS/ExperimentTwo 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 "TitleOS/ExperimentTwo" \ --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": "TitleOS/ExperimentTwo", "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 "TitleOS/ExperimentTwo" \ --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": "TitleOS/ExperimentTwo", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TitleOS/ExperimentTwo with Docker Model Runner:
docker model run hf.co/TitleOS/ExperimentTwo
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base_model:
- cognitivecomputations/samantha-1.2-mistral-7b
- mistralai/Mistral-7B-v0.1
- uukuguy/speechless-code-mistral-7b-v1.0
library_name: transformers
tags:
- mergekit
- merge
license: apache-2.0
---
# ExperimentV2 AKA NerdySamanthaV2 (Mistral v0.1 & Samantha v1.2 & Speechless Code Mistral v1.0 7B)
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 [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) as a base.
### Models Merged
The following models were included in the merge:
* [cognitivecomputations/samantha-1.2-mistral-7b](https://huggingface.co/cognitivecomputations/samantha-1.2-mistral-7b)
* [uukuguy/speechless-code-mistral-7b-v1.0](https://huggingface.co/uukuguy/speechless-code-mistral-7b-v1.0)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: mistralai/Mistral-7B-v0.1
- model: uukuguy/speechless-code-mistral-7b-v1.0
- model: cognitivecomputations/samantha-1.2-mistral-7b
merge_method: model_stock
base_model: mistralai/Mistral-7B-v0.1
dtype: bfloat16
```
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