Text Generation
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Safetensors
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Eval Results (legacy)
text-generation-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("acrastt/Puma-3B")
model = AutoModelForCausalLM.from_pretrained("acrastt/Puma-3B")Quick Links
This is OpenLLaMA 3B V2 finetuned on ShareGPT Hyperfiltered for 1 epochs.
Prompt template:
### HUMAN:
{prompt}
### RESPONSE:
<leave a newline for the model to answer>
GGML quants available here.
GPTQ quants available here.
Note: Don't expect this model to be good, I was just starting out to finetune. So don't roast me please!
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 41.02 |
| ARC (25-shot) | 41.3 |
| HellaSwag (10-shot) | 71.85 |
| MMLU (5-shot) | 27.51 |
| TruthfulQA (0-shot) | 38.34 |
| Winogrande (5-shot) | 66.38 |
| GSM8K (5-shot) | 0.76 |
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 41.02 |
| AI2 Reasoning Challenge (25-Shot) | 41.30 |
| HellaSwag (10-Shot) | 71.85 |
| MMLU (5-Shot) | 27.51 |
| TruthfulQA (0-shot) | 38.34 |
| Winogrande (5-shot) | 66.38 |
| GSM8k (5-shot) | 0.76 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard41.300
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard71.850
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard27.510
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard38.340
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard66.380
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard0.760
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="acrastt/Puma-3B")