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
metadata
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- openchat/openchat-3.5-0106
- machinists/Mistral-7B-SQL
model-index:
- name: jaLLAbi
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: 22.7
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/jaLLAbi
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: 25.04
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/jaLLAbi
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: 23.12
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/jaLLAbi
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: 0
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/jaLLAbi
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: 49.57
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/jaLLAbi
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: 0
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/jaLLAbi
name: Open LLM Leaderboard
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 |