Instructions to use willamazon1/sdft-search-lora-iter20 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use willamazon1/sdft-search-lora-iter20 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="willamazon1/sdft-search-lora-iter20")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("willamazon1/sdft-search-lora-iter20")
model = AutoModelForCausalLM.from_pretrained("willamazon1/sdft-search-lora-iter20", 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 willamazon1/sdft-search-lora-iter20 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "willamazon1/sdft-search-lora-iter20"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "willamazon1/sdft-search-lora-iter20",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/willamazon1/sdft-search-lora-iter20

SGLang

How to use willamazon1/sdft-search-lora-iter20 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 "willamazon1/sdft-search-lora-iter20" \
    --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": "willamazon1/sdft-search-lora-iter20",
		"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 "willamazon1/sdft-search-lora-iter20" \
        --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": "willamazon1/sdft-search-lora-iter20",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use willamazon1/sdft-search-lora-iter20 with Docker Model Runner:
```
docker model run hf.co/willamazon1/sdft-search-lora-iter20
```

sdft-search-lora-iter20 / README.md

willamazon1

Add merged SDFT+search-R1 LoRA (iter20) full model

e6dac3c verified 4 days ago

preview code

Raw

History Blame Contribute Delete

1.89 kB

	---
	license: apache-2.0
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- qwen3
	- reinforcement-learning
	- lora
	- search-r1
	- rag
	base_model: Qwen/Qwen3-8B-Base
	---

	# sdft-search-lora-iter20

	A Qwen3-8B model fine-tuned for retrieval-augmented (search-R1 style) multi-turn
	reasoning. This is a LoRA adapter merged back into the full model and exported
	as standard HuggingFace safetensors.

	## Training

	- Base / init: Qwen3-8B-Base after a supervised fine-tuning (SDFT) cold-start
	(oracle-mix SFT), then RL.
	- Method: Search-R1 style RL (GRPO/GSPO) in the [slime](https://github.com/THUDM/slime)
	framework, with a co-located GPU-faiss retriever over a Wikipedia-2018 index.
	- Parameter-efficient: LoRA, rank `r=16`, `alpha=32` (scaling `alpha/r = 2.0`),
	applied to `linear_qkv`, `linear_proj`, `linear_fc1`, `linear_fc2` in every layer.
	- Checkpoint: RL iteration 20. The adapter (all 144 `lora_B` factors nonzero)
	is merged into the base weights: `W ← W + (alpha/r) · B @ A` per target module.

	> Note: This is an early checkpoint (20 RL steps). The merged delta over the
	> SDFT base is small (relative Frobenius norm ~1e-2 per projection matrix), so the
	> model behaves very close to the SDFT base with an initial RL update applied.

	## Architecture

	Qwen3, 36 layers, hidden 4096, 32 attn heads / 8 KV heads (GQA), intermediate 12288,
	vocab 151936, bf16. Identical arch to Qwen3-8B-Base.

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	import torch

	tok = AutoTokenizer.from_pretrained("willamazon1/sdft-search-lora-iter20")
	model = AutoModelForCausalLM.from_pretrained(
	"willamazon1/sdft-search-lora-iter20", dtype=torch.bfloat16, device_map="cuda"
	)
	ids = tok("The capital of France is", return_tensors="pt").input_ids.cuda()
	print(tok.decode(model.generate(ids, max_new_tokens=16)[0]))
	```