Instructions to use willamazon1/Qwen3-8B-SDFT-Math-LoRA-new with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use willamazon1/Qwen3-8B-SDFT-Math-LoRA-new with Transformers:

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

pipe = pipeline("text-generation", model="willamazon1/Qwen3-8B-SDFT-Math-LoRA-new")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("willamazon1/Qwen3-8B-SDFT-Math-LoRA-new")
model = AutoModelForCausalLM.from_pretrained("willamazon1/Qwen3-8B-SDFT-Math-LoRA-new", 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/Qwen3-8B-SDFT-Math-LoRA-new with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "willamazon1/Qwen3-8B-SDFT-Math-LoRA-new"
# 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/Qwen3-8B-SDFT-Math-LoRA-new",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/willamazon1/Qwen3-8B-SDFT-Math-LoRA-new

SGLang

How to use willamazon1/Qwen3-8B-SDFT-Math-LoRA-new 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/Qwen3-8B-SDFT-Math-LoRA-new" \
    --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/Qwen3-8B-SDFT-Math-LoRA-new",
		"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/Qwen3-8B-SDFT-Math-LoRA-new" \
        --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/Qwen3-8B-SDFT-Math-LoRA-new",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use willamazon1/Qwen3-8B-SDFT-Math-LoRA-new with Docker Model Runner:
```
docker model run hf.co/willamazon1/Qwen3-8B-SDFT-Math-LoRA-new
```

Qwen3-8B-SDFT-Math-LoRA-new

Qwen3-8B (dense, 36 layers, 8.19B params) tuned for mathematical reasoning. This repo holds fully merged bf16 weights — the LoRA adapter has been folded into the base matrices, so it is a drop-in replacement for Qwen/Qwen3-8B-Base; no PEFT adapter loading is required.

Training pipeline

Base: Qwen/Qwen3-8B-Base.
SFT: supervised fine-tuning on a balanced oracle mixture (math / retrieval-augmented QA / tool-use dialogue).
Math RL, stage 1: GRPO on math problems with a verifiable answer-matching reward, training LoRA adapters (rank 16, alpha 32, scaling 2.0) on q/k/v, o_proj, gate/up_proj and down_proj of every layer. The stage-1 adapter was merged into the SFT weights.
Math RL, stage 2 (this checkpoint): RL continued from the stage-1 merged model for 60 more optimizer steps, again with a fresh LoRA adapter of the same shape, which is merged here.

Trained with slime on Megatron-LM (TP=4, CP=2, bf16).

Checkpoint details


Parameters	8.19 B
dtype	bfloat16
Tensors	399 (4 safetensors shards, 16.4 GB)
Vocab	151936 (Megatron embedding padding stripped)
Stage-2 relative weight change, per LoRA'd matrix (‖Δ‖/‖W‖)	2.8e-5 – 7.7e-5

Non-finite check: 0 NaN/Inf tensors across all shards.

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "willamazon1/Qwen3-8B-SDFT-Math-LoRA-new"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, dtype=torch.bfloat16).to("cuda")

prompt = (
    "Question: Natalia sold clips to 48 friends in April, and then she sold "
    "half as many clips in May. How many clips did Natalia sell altogether "
    "in April and May?\nAnswer:"
)
ids = tok(prompt, return_tensors="pt").input_ids.cuda()
out = model.generate(ids, max_new_tokens=128, do_sample=False)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

This is a base-style (completion) model, not an instruction-tuned chat model: prompt it with Question: ... \nAnswer: style completions rather than a chat template.

Limitations

Stage 2 is a short RL continuation (60 steps), so the weight delta over the stage-1 model is small. The model inherits the biases and knowledge cutoff of Qwen3-8B-Base, and its math answers are not guaranteed correct — verify outputs before relying on them.

Downloads last month: 268

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for willamazon1/Qwen3-8B-SDFT-Math-LoRA-new

Base model

Qwen/Qwen3-8B-Base

Adapter

(99)

this model