Instructions to use smolify/smolified-debug-run with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use smolify/smolified-debug-run with Transformers:

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

pipe = pipeline("text-generation", model="smolify/smolified-debug-run")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("smolify/smolified-debug-run")
model = AutoModelForImageTextToText.from_pretrained("smolify/smolified-debug-run")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use smolify/smolified-debug-run with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "smolify/smolified-debug-run"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "smolify/smolified-debug-run",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/smolify/smolified-debug-run

SGLang

How to use smolify/smolified-debug-run 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 "smolify/smolified-debug-run" \
    --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": "smolify/smolified-debug-run",
		"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 "smolify/smolified-debug-run" \
        --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": "smolify/smolified-debug-run",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use smolify/smolified-debug-run with Docker Model Runner:
```
docker model run hf.co/smolify/smolified-debug-run
```

rishiraj commited on Mar 25

Commit

53bf5ff

verified ·

1 Parent(s): 19dded1

Smolify: Intelligence Distilled.

Browse files

Files changed (1) hide show

README.md +60 -12

README.md CHANGED Viewed

@@ -1,21 +1,69 @@
 ---
-base_model: unsloth/Qwen3.5-0.8B
-tags:
-- text-generation-inference
-- transformers
-- unsloth
-- qwen3_5
 license: apache-2.0
 language:
 - en
 ---
-# Uploaded finetuned  model
-- **Developed by:** smolify
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/Qwen3.5-0.8B
-This qwen3_5 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)

 ---
 license: apache-2.0
 language:
 - en
+tags:
+- text-generation-inference
+- transformers
+- smolify
+- dslm
+pipeline_tag: text-generation
+inference:
+  parameters:
+    temperature: 1
+    top_p: 0.95
+    top_k: 64
 ---
+# 🤏 smolified-debug-run
+> **Intelligence, Distilled.**
+This is a **Domain Specific Language Model (DSLM)** generated by the **Smolify Foundry**.
+It has been synthetically distilled from SOTA reasoning engines into a high-efficiency architecture, optimized for deployment on edge hardware (CPU/NPU) or low-VRAM environments.
+## 📦 Asset Details
+- **Origin:** Smolify Foundry (Job ID: `DEBUG_RETRY`)
+- **Architecture:** qwen-3.5-0.8b
+- **Training Method:** Proprietary Neural Distillation
+- **Optimization:** 4-bit Quantized / FP16 Mixed
+- **Dataset:** [Link to Dataset](https://huggingface.co/datasets/smolify/smolified-debug-run)
+## 🚀 Usage (Inference)
+This model is compatible with standard inference backends like vLLM, and Hugging Face Transformers.
+```python
+# Example: Running your Sovereign Model
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_id = "smolify/smolified-debug-run"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
+messages = [
+    {"role": "system", "content": '''You are a highly intelligent AI.'''},
+    {"role": "user", "content": '''Can you solve problem number 0?'''}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize = False,
+    add_generation_prompt = True,
+)
+if "qwen-3.5-0.8b" == "gemma-3-270m":
+    text = text.removeprefix('<bos>')
+from transformers import TextStreamer
+_ = model.generate(
+    **tokenizer(text, return_tensors = "pt").to(model.device),
+    max_new_tokens = 1000,
+    temperature = 1.0, top_p = 0.95, top_k = 64,
+    streamer = TextStreamer(tokenizer, skip_prompt = True),
+)
+```
+## ⚖️ License & Ownership
+This model weights are a sovereign asset owned by **smolify**.
+Generated via [Smolify.ai](https://smolify.ai).
+[<img src="https://smolify.ai/smolify.gif" width="100"/>](https://smolify.ai)