Instructions to use migtissera/Tess-10.7B-v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use migtissera/Tess-10.7B-v1.5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="migtissera/Tess-10.7B-v1.5")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("migtissera/Tess-10.7B-v1.5") model = AutoModelForCausalLM.from_pretrained("migtissera/Tess-10.7B-v1.5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use migtissera/Tess-10.7B-v1.5 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "migtissera/Tess-10.7B-v1.5" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "migtissera/Tess-10.7B-v1.5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/migtissera/Tess-10.7B-v1.5
- SGLang
How to use migtissera/Tess-10.7B-v1.5 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 "migtissera/Tess-10.7B-v1.5" \ --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": "migtissera/Tess-10.7B-v1.5", "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 "migtissera/Tess-10.7B-v1.5" \ --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": "migtissera/Tess-10.7B-v1.5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use migtissera/Tess-10.7B-v1.5 with Docker Model Runner:
docker model run hf.co/migtissera/Tess-10.7B-v1.5
| license: apache-2.0 | |
| model-index: | |
| - name: Tess-10.7B-v1.5 | |
| results: | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: AI2 Reasoning Challenge (25-Shot) | |
| type: ai2_arc | |
| config: ARC-Challenge | |
| split: test | |
| args: | |
| num_few_shot: 25 | |
| metrics: | |
| - type: acc_norm | |
| value: 65.02 | |
| name: normalized accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=migtissera/Tess-10.7B-v1.5 | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: HellaSwag (10-Shot) | |
| type: hellaswag | |
| split: validation | |
| args: | |
| num_few_shot: 10 | |
| metrics: | |
| - type: acc_norm | |
| value: 84.07 | |
| name: normalized accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=migtissera/Tess-10.7B-v1.5 | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: MMLU (5-Shot) | |
| type: cais/mmlu | |
| config: all | |
| split: test | |
| args: | |
| num_few_shot: 5 | |
| metrics: | |
| - type: acc | |
| value: 65.09 | |
| name: accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=migtissera/Tess-10.7B-v1.5 | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: TruthfulQA (0-shot) | |
| type: truthful_qa | |
| config: multiple_choice | |
| split: validation | |
| args: | |
| num_few_shot: 0 | |
| metrics: | |
| - type: mc2 | |
| value: 47.43 | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=migtissera/Tess-10.7B-v1.5 | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: Winogrande (5-shot) | |
| type: winogrande | |
| config: winogrande_xl | |
| split: validation | |
| args: | |
| num_few_shot: 5 | |
| metrics: | |
| - type: acc | |
| value: 83.35 | |
| name: accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=migtissera/Tess-10.7B-v1.5 | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: GSM8k (5-shot) | |
| type: gsm8k | |
| config: main | |
| split: test | |
| args: | |
| num_few_shot: 5 | |
| metrics: | |
| - type: acc | |
| value: 54.36 | |
| name: accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=migtissera/Tess-10.7B-v1.5 | |
| name: Open LLM Leaderboard | |
| # Please use the updated version Tess-10.7B-v1.5b | |
| Tess-10.7B-v1.5b has hyperparameter optimizations. Access at: https://huggingface.co/migtissera/Tess-10.7B-v1.5b | |
| <br> | |
|  | |
| <br> | |
| Tess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-10.7B-v1.5 was trained on the SOLAR-10.7B base. | |
| # Prompt Format: | |
| ``` | |
| SYSTEM: <ANY SYSTEM CONTEXT> | |
| USER: | |
| ASSISTANT: | |
| ``` | |
| # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) | |
| Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Tess-10.7B-v1.5) | |
| | Metric |Value| | |
| |---------------------------------|----:| | |
| |Avg. |66.55| | |
| |AI2 Reasoning Challenge (25-Shot)|65.02| | |
| |HellaSwag (10-Shot) |84.07| | |
| |MMLU (5-Shot) |65.09| | |
| |TruthfulQA (0-shot) |47.43| | |
| |Winogrande (5-shot) |83.35| | |
| |GSM8k (5-shot) |54.36| | |