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
```

nielsr HF Staff commited on Feb 25, 2025

Commit

4dbdd39

verified ·

1 Parent(s): a199eef

Add text-generation pipeline tag and library name

Browse files

Adds `pipeline_tag: text-generation` and `library_name: transformers` to the model card.

Files changed (1) hide show

README.md +9 -6

README.md CHANGED Viewed

@@ -1,10 +1,9 @@
 ---
-license: apache-2.0
 language:
 - en
-base_model:
-- >-
-  MasterControlAIML/DeepSeek-R1-Strategy-Qwen-2.5-1.5b-Unstructured-To-Structured
 tags:
 - DeepSeek
 - R1
@@ -13,8 +12,11 @@ tags:
 - JSON
 - Unstructured
 - Structured
 ---
 # 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)
@@ -332,7 +334,7 @@ You are an expert data extractor specializing in mapping hierarchical text data
 ### STRICT RULES FOR GENERATING OUTPUT:
 1. **Both Tags Required:**
-   - Always provide both the `<think>` and `<answer>` sections.
    - If reasoning is minimal, state: "Direct mapping from text to schema."
 2. **JSON Schema Mapping:**
    - Strictly map the text data to the given JSON Schema without modification or omissions.
@@ -688,4 +690,5 @@ This project is licensed under the [Apache-2.0 License](LICENSE).
 ---
-Enjoy using the model and these examples as a reference for building your own prompt-driven applications! Happy coding!

 ---
+base_model:
+- MasterControlAIML/DeepSeek-R1-Strategy-Qwen-2.5-1.5b-Unstructured-To-Structured
 language:
 - en
+license: apache-2.0
 tags:
 - DeepSeek
 - R1
 - JSON
 - Unstructured
 - Structured
+pipeline_tag: text-generation
+library_name: transformers
 ---
+```markdown
 # 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)
 ### STRICT RULES FOR GENERATING OUTPUT:
 1. **Both Tags Required:**
+   - Always provide both the `<think>` and the `<answer>` sections.
    - If reasoning is minimal, state: "Direct mapping from text to schema."
 2. **JSON Schema Mapping:**
    - Strictly map the text data to the given JSON Schema without modification or omissions.
 ---
+Enjoy using the model and these examples as a reference for building your own prompt-driven applications! Happy coding!
+```