Instructions to use evenfarther/DeepSeek-R1-Distill-Qwen-14b-chemical-synthesis-adapter-high-TPR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use evenfarther/DeepSeek-R1-Distill-Qwen-14b-chemical-synthesis-adapter-high-TPR with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/DATA/deepseek_r1_distilled_qwen3_14b/models/DeepSeek-R1-Distill-Qwen3-14B") model = PeftModel.from_pretrained(base_model, "evenfarther/DeepSeek-R1-Distill-Qwen-14b-chemical-synthesis-adapter-high-TPR") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -30,7 +30,7 @@ This repository contains the LoRA adapter for a DeepSeek‑R1‑Distill‑Qwen
|
|
| 30 |
- Dataset (train): `train_pu_hem.jsonl` (316,442 samples)
|
| 31 |
- Dataset (val): `validate_pu_hem.jsonl` (79,114 samples)
|
| 32 |
- Adapter size: ~1.10 GB (`adapter_model.safetensors`)
|
| 33 |
-
- Checkpoint: Epoch 2 — `checkpoint-496` (best
|
| 34 |
|
| 35 |
The checkpoint includes a `chat_template.jinja` to ensure prompt formatting matches SFT conditions.
|
| 36 |
|
|
|
|
| 30 |
- Dataset (train): `train_pu_hem.jsonl` (316,442 samples)
|
| 31 |
- Dataset (val): `validate_pu_hem.jsonl` (79,114 samples)
|
| 32 |
- Adapter size: ~1.10 GB (`adapter_model.safetensors`)
|
| 33 |
+
- Checkpoint: Epoch 2 — `checkpoint-496` (best TPR among evaluated epochs)
|
| 34 |
|
| 35 |
The checkpoint includes a `chat_template.jinja` to ensure prompt formatting matches SFT conditions.
|
| 36 |
|