Text Generation
PEFT
Safetensors
English
lora
qlora
sft
trl
text-to-sql
sql
conversational
Eval Results (legacy)
Instructions to use SASVAAI/GLM-4.7-Flash-sql-create-context with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SASVAAI/GLM-4.7-Flash-sql-create-context with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("zai-org/GLM-4.7-Flash") model = PeftModel.from_pretrained(base_model, "SASVAAI/GLM-4.7-Flash-sql-create-context") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from SASVAAI/GLM-4.7-Flash-sql-create-context: direct link, hf CLI and curl.
- Browser
- Download file 190 Bytes
-
https://huggingface.co/SASVAAI/GLM-4.7-Flash-sql-create-context/resolve/main/train_results.json
- Command line
-
hf download hf://SASVAAI/GLM-4.7-Flash-sql-create-context/train_results.json
-
curl -L -o train_results.json https://huggingface.co/SASVAAI/GLM-4.7-Flash-sql-create-context/resolve/main/train_results.json
190 Bytes
| { | |
| "total_flos": 2.3441226939026637e+17, | |
| "train_loss": 0.5646523337894016, | |
| "train_runtime": 6392.6844, | |
| "train_samples_per_second": 1.689, | |
| "train_steps_per_second": 0.106 | |
| } |