File size: 1,420 Bytes
01a2752
8fd9ceb
 
 
 
 
 
 
 
 
 
01a2752
8fd9ceb
01a2752
 
8fd9ceb
01a2752
8fd9ceb
 
 
01a2752
 
 
8fd9ceb
 
 
 
 
01a2752
8fd9ceb
01a2752
8fd9ceb
 
 
 
01a2752
8fd9ceb
 
01a2752
8fd9ceb
01a2752
8fd9ceb
01a2752
8fd9ceb
 
 
 
01a2752
8fd9ceb
 
01a2752
8fd9ceb
 
 
 
01a2752
8fd9ceb
01a2752
8fd9ceb
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
---
license: apache-2.0
language:
  - en
tags:
  - text-to-legend
  - pure
  - legend
  - tokenformer
  - text-generation
base_model: Qwen/Qwen3-14B
library_name: transformers
pipeline_tag: text-generation
---

# Legend Query Generation

Fine-tuned from [Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B) using
[ScalarLM](https://github.com/supermassive-intelligence/scalarlm) with
tokenformer adapters.

## Model Details

- **Base model**: Qwen/Qwen3-14B
- **Fine-tuning method**: Tokenformer adapters (additive MLP adapters with learned attention)
- **Checkpoint step**: 6656
- **Trained parameters**: 442 (attention projections, layernorms, embeddings, tokenformer adapters)
- **Tokenformer config**: num_heads=4, r=32

## How to Use

This model uses custom tokenformer adapter layers on top of the base model.
It requires the `TokenformerAdapter` module for inference. Standard
`AutoModelForCausalLM.from_pretrained` will not load the adapter weights
correctly.

See the [ScalarLM repository](https://github.com/supermassive-intelligence/scalarlm)
for inference instructions.

## Training

Trained on Legend query generation data using the Qwen chat template format.

**Prompt format:**
```
<|im_start|>user
{question}

Here is some schema information:
{schema_text}

First, plan how to write the query, then write it in a ```pure SQL``` code block.
<|im_end|>
<|im_start|>assistant
```

## License

Apache 2.0