Text Generation
Transformers
Safetensors
English
qwen3
text-to-legend
pure
legend
tokenformer
conversational
text-generation-inference
Instructions to use Sudnya/Qwen3-14B-legend-10K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sudnya/Qwen3-14B-legend-10K with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Sudnya/Qwen3-14B-legend-10K") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Sudnya/Qwen3-14B-legend-10K") model = AutoModelForCausalLM.from_pretrained("Sudnya/Qwen3-14B-legend-10K", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Sudnya/Qwen3-14B-legend-10K with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Sudnya/Qwen3-14B-legend-10K" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Sudnya/Qwen3-14B-legend-10K", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Sudnya/Qwen3-14B-legend-10K
- SGLang
How to use Sudnya/Qwen3-14B-legend-10K with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Sudnya/Qwen3-14B-legend-10K" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Sudnya/Qwen3-14B-legend-10K", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Sudnya/Qwen3-14B-legend-10K" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Sudnya/Qwen3-14B-legend-10K", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Sudnya/Qwen3-14B-legend-10K with Docker Model Runner:
docker model run hf.co/Sudnya/Qwen3-14B-legend-10K
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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
|