Text Generation
Transformers
Safetensors
mixtral
Merge
mergekit
lazymergekit
openchat/openchat-3.5-0106
machinists/Mistral-7B-SQL
Eval Results (legacy)
text-generation-inference
Instructions to use AbacusResearch/jaLLAbi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AbacusResearch/jaLLAbi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AbacusResearch/jaLLAbi")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AbacusResearch/jaLLAbi") model = AutoModelForCausalLM.from_pretrained("AbacusResearch/jaLLAbi") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use AbacusResearch/jaLLAbi with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AbacusResearch/jaLLAbi" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AbacusResearch/jaLLAbi", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AbacusResearch/jaLLAbi
- SGLang
How to use AbacusResearch/jaLLAbi 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 "AbacusResearch/jaLLAbi" \ --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": "AbacusResearch/jaLLAbi", "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 "AbacusResearch/jaLLAbi" \ --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": "AbacusResearch/jaLLAbi", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AbacusResearch/jaLLAbi with Docker Model Runner:
docker model run hf.co/AbacusResearch/jaLLAbi
jaLLAbi
jaLLAbi is a merge of the following models using mergekit:
🧩 Configuration
```yaml slices:
- sources:
- model: openchat/openchat-3.5-0106 layer_range: [0, 32]
- model: machinists/Mistral-7B-SQL layer_range: [0, 32]
merge_method: slerp base_model: openchat/openchat-3.5-0106 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 20.07 |
| AI2 Reasoning Challenge (25-Shot) | 22.70 |
| HellaSwag (10-Shot) | 25.04 |
| MMLU (5-Shot) | 23.12 |
| TruthfulQA (0-shot) | 0.00 |
| Winogrande (5-shot) | 49.57 |
| GSM8k (5-shot) | 0.00 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard22.700
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard25.040
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard23.120
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard0.000
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard49.570
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard0.000