deepseek-coder-1.3b-chat-and-function-calling
It was created by starting with the deepseek-coder-1.3b and training it on the open assistant dataset then training yhat on function calling. We have attached the wandb report in pdf form to view the training run at a glance.
Reson
This model was fine tuned to allow it to work with the openai syntask and will return function when apperate.
Templete
Us the following templete when interacting with the fine tuned model.
Referrals
Run Pod - This is who I use to train th emodels on huggingface. If you use it we both get free crdits. - Visit Runpod's Website!
Paypal - If you want to leave a tip, it is appecaheted. - Visit My Paypal!
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 31.82 |
| AI2 Reasoning Challenge (25-Shot) | 26.28 |
| HellaSwag (10-Shot) | 39.27 |
| MMLU (5-Shot) | 26.92 |
| TruthfulQA (0-shot) | 43.37 |
| Winogrande (5-shot) | 51.70 |
| GSM8k (5-shot) | 3.41 |
- Downloads last month
- 71
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard26.280
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard39.270
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard26.920
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard43.370
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard51.700
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard3.410