Text Generation
Transformers
Safetensors
mistral
mergekit
Merge
Eval Results (legacy)
text-generation-inference
Instructions to use ConvexAI/Pelican-9b-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ConvexAI/Pelican-9b-v0.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ConvexAI/Pelican-9b-v0.1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ConvexAI/Pelican-9b-v0.1") model = AutoModelForCausalLM.from_pretrained("ConvexAI/Pelican-9b-v0.1") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ConvexAI/Pelican-9b-v0.1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ConvexAI/Pelican-9b-v0.1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ConvexAI/Pelican-9b-v0.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ConvexAI/Pelican-9b-v0.1
- SGLang
How to use ConvexAI/Pelican-9b-v0.1 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 "ConvexAI/Pelican-9b-v0.1" \ --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": "ConvexAI/Pelican-9b-v0.1", "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 "ConvexAI/Pelican-9b-v0.1" \ --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": "ConvexAI/Pelican-9b-v0.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ConvexAI/Pelican-9b-v0.1 with Docker Model Runner:
docker model run hf.co/ConvexAI/Pelican-9b-v0.1
merge
This is a merge of pre-trained language models created using mergekit.
⚠️Warning ⚠️
Model is broken and outputs only broken german. Possibly obsessed with Fußball. ⚽
Merge Method
This model was merged using the passthrough merge method and only speaks german, somewhat obsessed with football.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
slices:
- sources:
- model: flemmingmiguel/MBX-7B-v3
layer_range: [0, 32]
- sources:
- model: flemmingmiguel/MBX-7B
layer_range: [20, 32]
merge_method: passthrough
dtype: float16
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 50.38 |
| AI2 Reasoning Challenge (25-Shot) | 47.95 |
| HellaSwag (10-Shot) | 66.22 |
| MMLU (5-Shot) | 62.85 |
| TruthfulQA (0-shot) | 50.61 |
| Winogrande (5-shot) | 74.66 |
| GSM8k (5-shot) | 0.00 |
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Model tree for ConvexAI/Pelican-9b-v0.1
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard47.950
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard66.220
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard62.850
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard50.610
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard74.660
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard0.000