Instructions to use nlpguy/T3QM7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nlpguy/T3QM7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nlpguy/T3QM7")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nlpguy/T3QM7") model = AutoModelForCausalLM.from_pretrained("nlpguy/T3QM7") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use nlpguy/T3QM7 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nlpguy/T3QM7" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nlpguy/T3QM7", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nlpguy/T3QM7
- SGLang
How to use nlpguy/T3QM7 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 "nlpguy/T3QM7" \ --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": "nlpguy/T3QM7", "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 "nlpguy/T3QM7" \ --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": "nlpguy/T3QM7", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use nlpguy/T3QM7 with Docker Model Runner:
docker model run hf.co/nlpguy/T3QM7
| base_model: | |
| - liminerity/M7-7b | |
| - chihoonlee10/T3Q-Mistral-Orca-Math-DPO | |
| library_name: transformers | |
| tags: | |
| - mergekit | |
| - merge | |
| license: apache-2.0 | |
| # merged | |
| 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: | |
| * [liminerity/M7-7b](https://huggingface.co/liminerity/M7-7b) | |
| * [chihoonlee10/T3Q-Mistral-Orca-Math-DPO](https://huggingface.co/chihoonlee10/T3Q-Mistral-Orca-Math-DPO) | |
| ### Configuration | |
| The following YAML configuration was used to produce this model: | |
| ```yaml | |
| base_model: | |
| model: | |
| path: chihoonlee10/T3Q-Mistral-Orca-Math-DPO | |
| dtype: float16 | |
| merge_method: slerp | |
| parameters: | |
| t: | |
| - filter: self_attn | |
| value: [0.0, 0.5, 0.3, 0.7, 1.0] | |
| - filter: mlp | |
| value: [1.0, 0.5, 0.7, 0.3, 0.0] | |
| - value: 0.4 | |
| slices: | |
| - sources: | |
| - layer_range: [0, 32] | |
| model: | |
| model: | |
| path: liminerity/M7-7b | |
| - layer_range: [0, 32] | |
| model: | |
| model: | |
| path: chihoonlee10/T3Q-Mistral-Orca-Math-DPO | |
| ``` |