Text Generation
Transformers
Safetensors
English
mistral
qlora
dto
Eval Results (legacy)
text-generation-inference
Instructions to use senseable/garten2-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use senseable/garten2-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="senseable/garten2-7b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("senseable/garten2-7b") model = AutoModelForCausalLM.from_pretrained("senseable/garten2-7b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use senseable/garten2-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "senseable/garten2-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "senseable/garten2-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/senseable/garten2-7b
- SGLang
How to use senseable/garten2-7b 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 "senseable/garten2-7b" \ --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": "senseable/garten2-7b", "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 "senseable/garten2-7b" \ --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": "senseable/garten2-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use senseable/garten2-7b with Docker Model Runner:
docker model run hf.co/senseable/garten2-7b
| language: | |
| - en | |
| license: apache-2.0 | |
| library_name: transformers | |
| tags: | |
| - qlora | |
| - dto | |
| base_model: | |
| - mistralai/Mistral-7B-v0.1 | |
| model-index: | |
| - name: garten2-7b | |
| 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: 69.37 | |
| name: normalized accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=senseable/garten2-7b | |
| 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: 87.54 | |
| name: normalized accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=senseable/garten2-7b | |
| 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: 65.44 | |
| name: accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=senseable/garten2-7b | |
| 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: 59.5 | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=senseable/garten2-7b | |
| 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: 84.69 | |
| name: accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=senseable/garten2-7b | |
| 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: 69.37 | |
| name: accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=senseable/garten2-7b | |
| name: Open LLM Leaderboard | |
| # Details | |
| Introducing Garten2-7B, a cutting-edge, small 7B all-purpose Language Model (LLM), designed to redefine the boundaries of artificial intelligence in natural language understanding and generation. Garten2-7B stands out with its unique architecture, expertly crafted to deliver exceptional performance in a wide array of tasks, from conversation to content creation. | |
| # [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_senseable__garten2-7b) | |
| | Metric |Value| | |
| |---------------------------------|----:| | |
| |Avg. |72.65| | |
| |AI2 Reasoning Challenge (25-Shot)|69.37| | |
| |HellaSwag (10-Shot) |87.54| | |
| |MMLU (5-Shot) |65.44| | |
| |TruthfulQA (0-shot) |59.50| | |
| |Winogrande (5-shot) |84.69| | |
| |GSM8k (5-shot) |69.37| | |