Instructions to use MasterControlAIML/DeepSeek-R1-Qwen2.5-3b-LLM-Judge-Reward-JSON-Unstructured-To-Structured-Lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MasterControlAIML/DeepSeek-R1-Qwen2.5-3b-LLM-Judge-Reward-JSON-Unstructured-To-Structured-Lora with Transformers:

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

pipe = pipeline("text-generation", model="MasterControlAIML/DeepSeek-R1-Qwen2.5-3b-LLM-Judge-Reward-JSON-Unstructured-To-Structured-Lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MasterControlAIML/DeepSeek-R1-Qwen2.5-3b-LLM-Judge-Reward-JSON-Unstructured-To-Structured-Lora")
model = AutoModelForCausalLM.from_pretrained("MasterControlAIML/DeepSeek-R1-Qwen2.5-3b-LLM-Judge-Reward-JSON-Unstructured-To-Structured-Lora")
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 MasterControlAIML/DeepSeek-R1-Qwen2.5-3b-LLM-Judge-Reward-JSON-Unstructured-To-Structured-Lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MasterControlAIML/DeepSeek-R1-Qwen2.5-3b-LLM-Judge-Reward-JSON-Unstructured-To-Structured-Lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MasterControlAIML/DeepSeek-R1-Qwen2.5-3b-LLM-Judge-Reward-JSON-Unstructured-To-Structured-Lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MasterControlAIML/DeepSeek-R1-Qwen2.5-3b-LLM-Judge-Reward-JSON-Unstructured-To-Structured-Lora

SGLang

How to use MasterControlAIML/DeepSeek-R1-Qwen2.5-3b-LLM-Judge-Reward-JSON-Unstructured-To-Structured-Lora 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 "MasterControlAIML/DeepSeek-R1-Qwen2.5-3b-LLM-Judge-Reward-JSON-Unstructured-To-Structured-Lora" \
    --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": "MasterControlAIML/DeepSeek-R1-Qwen2.5-3b-LLM-Judge-Reward-JSON-Unstructured-To-Structured-Lora",
		"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 "MasterControlAIML/DeepSeek-R1-Qwen2.5-3b-LLM-Judge-Reward-JSON-Unstructured-To-Structured-Lora" \
        --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": "MasterControlAIML/DeepSeek-R1-Qwen2.5-3b-LLM-Judge-Reward-JSON-Unstructured-To-Structured-Lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use MasterControlAIML/DeepSeek-R1-Qwen2.5-3b-LLM-Judge-Reward-JSON-Unstructured-To-Structured-Lora with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for MasterControlAIML/DeepSeek-R1-Qwen2.5-3b-LLM-Judge-Reward-JSON-Unstructured-To-Structured-Lora to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for MasterControlAIML/DeepSeek-R1-Qwen2.5-3b-LLM-Judge-Reward-JSON-Unstructured-To-Structured-Lora to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MasterControlAIML/DeepSeek-R1-Qwen2.5-3b-LLM-Judge-Reward-JSON-Unstructured-To-Structured-Lora to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="MasterControlAIML/DeepSeek-R1-Qwen2.5-3b-LLM-Judge-Reward-JSON-Unstructured-To-Structured-Lora",
    max_seq_length=2048,
)

Docker Model Runner
How to use MasterControlAIML/DeepSeek-R1-Qwen2.5-3b-LLM-Judge-Reward-JSON-Unstructured-To-Structured-Lora with Docker Model Runner:
```
docker model run hf.co/MasterControlAIML/DeepSeek-R1-Qwen2.5-3b-LLM-Judge-Reward-JSON-Unstructured-To-Structured-Lora
```

bhaviktheslider commited on Jun 17, 2025

Commit

22aa838

verified ·

1 Parent(s): a2fc555

Update README.md

Browse files

Files changed (1) hide show

README.md +69 -8

README.md CHANGED Viewed

@@ -1,23 +1,84 @@
 ---
-base_model: unsloth/Qwen2.5-7B-Instruct
 tags:
 - text-generation-inference
 - transformers
 - unsloth
 - qwen2
 - trl
-- grpo
 license: apache-2.0
 language:
 - en
 ---
-# Uploaded  model
-- **Developed by:** bhaviktheslider
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/Qwen2.5-7B-Instruct
-This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

 ---
+# 🦄 Model Card
+base_model: unsloth/Qwen2.5-3B-Instruct
 tags:
 - text-generation-inference
 - transformers
 - unsloth
 - qwen2
 - trl
+- grpo   # Gradient Reward Policy Optimization
 license: apache-2.0
 language:
 - en
 ---
+# 📦 Uploaded Model
+| **Field**             | **Value**                                  |
+|-----------------------|--------------------------------------------|
+| **Developed by**      | **bhaviktheslider**                        |
+| **License**           | Apache 2.0                                 |
+| **Finetuned from**    | `unsloth/Qwen2.5-3B-Instruct`              |
+| **Training Framework**| [Unsloth](https://github.com/unslothai/unsloth) × Hugging Face TRL |
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+---
+## 🚀 What’s New?
+> **TL;DR** – Think of this model as the beefed-up, protein-shake-powered sequel to **MasterControlAIML/DeepSeek-R1-Qwen2.5-1.5b-SFT-R1-JSON-Unstructured-To-Structured** … except we ditched the SFT and let a squad of reward functions do the coaching.
+### Key Upgrades
+1. **Larger Backbone** – We jumped from a 1.5 B parameter model to a 3 B parameter **Qwen 2.5** variant for more representational oomph.
+2. **No SFT, All 🍬 Rewards** – Instead of supervised fine-tuning, training relied solely on reward-based optimization (GRPO).
+   - **LM-as-Judge**: A language model scored candidate outputs for task quality.
+   - **Auxiliary Rewards**: Style, length, and JSON-validity rewards kept the model on its best behavior.
+3. **2× Faster Training** – Courtesy of Unsloth’s memory-efficient tricks (flash attention + fused optimizers).
+---
+## 🛠️ Intended Use
+- Converts messy, free-form text into structured JSON—exactly like its 1.5 B predecessor, but with a deeper knowledge reservoir and reinforcement-tuned precision.
+- Drop-in replacement for any pipeline already using the DeepSeek-R1 model. Just swap checkpoints and enjoy the headroom.
+---
+## 🏋️ Training Details
+| Item | Value |
+|------|-------|
+| **Base Model** | `unsloth/Qwen2.5-3B-Instruct` |
+| **Batching** | Gradient Accumulation 8, bfloat16 |
+| **Optimizer** | AdamW (fused) |
+| **Algorithm** | GRPO (policy ≈ LM; reward model ≈ separate LM judge) |
+| **Epochs** | 3 (effective) |
+| **Speed** | ~2× faster vs. vanilla PyTorch thanks to Unsloth |
+---
+## 📊 Evaluation (Coming Soon)
+We’re benchmarking against:
+- Exact-match JSON accuracy
+- Structural F1
+- Valid-JSON rate
+…stay tuned—numbers arriving faster than you can say “schema validation.”
+---
+## 🤝 Citation
+If you build something cool with this model, a shout-out would be lovely:
+```bibtex
+@misc{bhaviktheslider_2025_unsloth_qwen2.5_3b_grpo,
+  title  = {An Unsloth-accelerated GRPO-trained Qwen 2.5 3B for JSON structuring},
+  author = {Bhaviktheslider},
+  year   = {2025},
+  howpublished = {Hugging Face},
+  note   = {https://huggingface.co/bhaviktheslider/<repo>}
+}