Qwen3-8B JEE SDPO — MLX 4-bit
A 4-bit quantized MLX version of vipsehgal/qwen3-8b-jee-sdpo, optimized for fast inference on Apple Silicon Macs.
This model solves IIT JEE Advanced problems in Physics, Chemistry, and Mathematics with step-by-step chain-of-thought reasoning.
Model Details
| Property | Value |
|---|---|
| Base Model | Qwen3-8B |
| Training | SFT (QLoRA on Mac) → SDPO/DPO (A100 on cloud) |
| Quantization | 4-bit (group size 64), 4.5 bits/weight avg |
| Size | ~4.3 GB |
| Peak Memory | ~4.8 GB |
| Speed | ~30 tokens/sec on M3 Pro |
Training Pipeline
- SFT (Phase 2): QLoRA fine-tuning on 3,515 JEE + competition math examples using MLX on Apple M3 Pro
- SDPO (Phase 3): DPO preference optimization on A100 GPU using rollout-based preference pairs with LLM-as-judge feedback
- Quantization (Phase 4): Converted to MLX 4-bit for efficient local inference
Usage
Quick Start
pip install "mlx-lm[generate]"
mlx_lm.generate \
--model vipsehgal/qwen3-8b-jee-sdpo-mlx-4bit \
--prompt "Solve: A particle of mass m is projected with velocity v at 45 degrees. Find the angular momentum about the point of projection at maximum height."
Chat Format
mlx_lm.generate \
--model vipsehgal/qwen3-8b-jee-sdpo-mlx-4bit \
--prompt '<|im_start|>system
You are an expert IIT JEE tutor. Solve problems step-by-step using LaTeX notation. Show all work clearly and arrive at the final answer.<|im_end|>
<|im_start|>user
Find the number of real solutions of x^3 - 3x + 1 = 0<|im_end|>
<|im_start|>assistant
'
Local API Server
mlx_lm.server --model vipsehgal/qwen3-8b-jee-sdpo-mlx-4bit --port 8080
# Then query with any OpenAI-compatible client:
curl http://localhost:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{"messages": [{"role": "user", "content": "Solve: ..."}]}'
Python
from mlx_lm import load, generate
model, tokenizer = load("vipsehgal/qwen3-8b-jee-sdpo-mlx-4bit")
response = generate(
model, tokenizer,
prompt="Solve: The number of sp2 hybridised carbon atoms in benzaldehyde is:",
max_tokens=512,
)
print(response)
System Prompt
You are an expert IIT JEE tutor. Solve problems step-by-step using LaTeX notation. Show all work clearly and arrive at the final answer.
Requirements
- Apple Silicon Mac (M1/M2/M3/M4)
- ~5 GB free RAM
- Python 3.10+
pip install mlx-lm
License
Apache 2.0 (following Qwen3-8B license)
- Downloads last month
- 8
Model size
1B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
Log In to add your hardware
4-bit
Model tree for vipsehgal/qwen3-8b-jee-sdpo-mlx-4bit
Base model
Qwen/Qwen3-8B-MLX-4bit Adapter
vipsehgal/qwen3-8b-jee-sft Finetuned
vipsehgal/qwen3-8b-jee-sdpo