Instructions to use MasterControlAIML/DeepSeek-R1-Qwen2.5-1.5b-SFT-R1-JSON-Unstructured-To-Structured with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MasterControlAIML/DeepSeek-R1-Qwen2.5-1.5b-SFT-R1-JSON-Unstructured-To-Structured with Transformers:

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

pipe = pipeline("text-generation", model="MasterControlAIML/DeepSeek-R1-Qwen2.5-1.5b-SFT-R1-JSON-Unstructured-To-Structured")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MasterControlAIML/DeepSeek-R1-Qwen2.5-1.5b-SFT-R1-JSON-Unstructured-To-Structured")
model = AutoModelForCausalLM.from_pretrained("MasterControlAIML/DeepSeek-R1-Qwen2.5-1.5b-SFT-R1-JSON-Unstructured-To-Structured")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MasterControlAIML/DeepSeek-R1-Qwen2.5-1.5b-SFT-R1-JSON-Unstructured-To-Structured with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MasterControlAIML/DeepSeek-R1-Qwen2.5-1.5b-SFT-R1-JSON-Unstructured-To-Structured"
# 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-Qwen2.5-1.5b-SFT-R1-JSON-Unstructured-To-Structured",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MasterControlAIML/DeepSeek-R1-Qwen2.5-1.5b-SFT-R1-JSON-Unstructured-To-Structured

SGLang

How to use MasterControlAIML/DeepSeek-R1-Qwen2.5-1.5b-SFT-R1-JSON-Unstructured-To-Structured 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-Qwen2.5-1.5b-SFT-R1-JSON-Unstructured-To-Structured" \
    --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-Qwen2.5-1.5b-SFT-R1-JSON-Unstructured-To-Structured",
		"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-Qwen2.5-1.5b-SFT-R1-JSON-Unstructured-To-Structured" \
        --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-Qwen2.5-1.5b-SFT-R1-JSON-Unstructured-To-Structured",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MasterControlAIML/DeepSeek-R1-Qwen2.5-1.5b-SFT-R1-JSON-Unstructured-To-Structured with Docker Model Runner:
```
docker model run hf.co/MasterControlAIML/DeepSeek-R1-Qwen2.5-1.5b-SFT-R1-JSON-Unstructured-To-Structured
```

bhaviktheslider commited on Feb 7, 2025

Commit

70e7ffe

verified ·

1 Parent(s): 78cce86

Update README.md

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README.md +5 -5

README.md CHANGED Viewed

@@ -1,7 +1,7 @@
 MasterControl
 ---
-# MasterControlAIML R1-Qwen2.5-1.5b SFT R1 JSON Unstructured-To-Structured LoRA Model
 [![Unsloth](https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png)](https://github.com/unslothai/unsloth)
@@ -77,7 +77,7 @@ The Unsloth library allows you to quickly load and run inference with the model.
 from unsloth import FastLanguageModel
 import torch
-MODEL = "MasterControlAIML/R1-Qwen2.5-1.5b-SFT-R1-JSON-Unstructured-To-Structured-lora"
 # Load model and tokenizer
 model, tokenizer = FastLanguageModel.from_pretrained(
@@ -99,7 +99,7 @@ Below is an instruction that describes a task, paired with an input that provide
 """
 # Example instruction and prompt
-instruction = "Provide a summary of the Quality Assurance Manual."
 prompt = ALPACA_PROMPT.format(instruction, "")
 inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
 output = model.generate(**inputs, max_new_tokens=2000)
@@ -118,7 +118,7 @@ Alternatively, you can use Hugging Face's Transformers directly:
 from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
 import torch
-MODEL = "MasterControlAIML/R1-Qwen2.5-1.5b-SFT-R1-JSON-Unstructured-To-Structured-lora"
 # Initialize tokenizer and model
 tokenizer = AutoTokenizer.from_pretrained(MODEL)
@@ -132,7 +132,7 @@ Below is an instruction that describes a task, paired with an input that provide
 {}
 """
-TEXT = "Provide a detailed explanation of the QA processes in manufacturing."
 prompt = ALPACA_PROMPT.format(TEXT, "")
 inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
 text_streamer = TextStreamer(tokenizer)

 MasterControl
 ---
+# MasterControlAIML R1-Qwen2.5-1.5b SFT R1 JSON Unstructured-To-Structured Model
 [![Unsloth](https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png)](https://github.com/unslothai/unsloth)
 from unsloth import FastLanguageModel
 import torch
+MODEL = "MasterControlAIML/DeepSeek-R1-Qwen2.5-1.5b-SFT-R1-JSON-Unstructured-To-Structured"
 # Load model and tokenizer
 model, tokenizer = FastLanguageModel.from_pretrained(
 """
 # Example instruction and prompt
+instruction = "" (see examples below)
 prompt = ALPACA_PROMPT.format(instruction, "")
 inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
 output = model.generate(**inputs, max_new_tokens=2000)
 from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
 import torch
+MODEL = "MasterControlAIML/DeepSeek-R1-Qwen2.5-1.5b-SFT-R1-JSON-Unstructured-To-Structured"
 # Initialize tokenizer and model
 tokenizer = AutoTokenizer.from_pretrained(MODEL)
 {}
 """
+TEXT = ""(see examples below)
 prompt = ALPACA_PROMPT.format(TEXT, "")
 inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
 text_streamer = TextStreamer(tokenizer)