Instructions to use programmerGodbyte/smolified-tiny-text-to-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use programmerGodbyte/smolified-tiny-text-to-code with Transformers:

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

pipe = pipeline("text-generation", model="programmerGodbyte/smolified-tiny-text-to-code")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("programmerGodbyte/smolified-tiny-text-to-code", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use programmerGodbyte/smolified-tiny-text-to-code with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "programmerGodbyte/smolified-tiny-text-to-code"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "programmerGodbyte/smolified-tiny-text-to-code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/programmerGodbyte/smolified-tiny-text-to-code

SGLang

How to use programmerGodbyte/smolified-tiny-text-to-code 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 "programmerGodbyte/smolified-tiny-text-to-code" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "programmerGodbyte/smolified-tiny-text-to-code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "programmerGodbyte/smolified-tiny-text-to-code" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "programmerGodbyte/smolified-tiny-text-to-code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use programmerGodbyte/smolified-tiny-text-to-code with Docker Model Runner:
```
docker model run hf.co/programmerGodbyte/smolified-tiny-text-to-code
```

smolified-tiny-text-to-code / README.md

programmerGodbyte

Smolify: Intelligence Distilled.

e01a7e4 verified 5 months ago

preview code

Raw

History Blame Contribute Delete

2.49 kB

	---
	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-tiny-text-to-code

	> 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: `fe9b19bf`)
	- Architecture: DSLM-Micro (270M Parameter Class)
	- Training Method: Proprietary Neural Distillation
	- Optimization: 4-bit Quantized / FP16 Mixed
	- Dataset: [Link to Dataset](https://huggingface.co/datasets/programmerGodbyte/smolified-tiny-text-to-code)

	## 🚀 Usage (Inference)
	This model is compatible with standard inference backends like vLLM.

	```python
	# Example: Running your Sovereign Model
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = "programmerGodbyte/smolified-tiny-text-to-code"
	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

	messages = [
	{'role': 'system', 'content': '''The user will provide a natural language description of a programming task. Your goal is to generate correct, runnable Python code that solves the task. Adhere to PEP 8 style guidelines. Include type hints for all functions and variables. The code should be self-contained and ready to run.'''},
	{'role': 'user', 'content': '''Create a Python function named `factorial` that calculates the factorial of a non-negative integer. If the input is negative, it should raise a `ValueError`. If the input is 0, it should return 1.'''}
	]
	text = tokenizer.apply_chat_template(
	messages,
	tokenize = False,
	add_generation_prompt = True,
	).removeprefix('<bos>')

	from transformers import TextStreamer
	_ = model.generate(
	**tokenizer(text, return_tensors = "pt").to("cuda"),
	max_new_tokens = 1000,
	temperature = 1, top_p = 0.95, top_k = 64,
	streamer = TextStreamer(tokenizer, skip_prompt = True),
	)
	```

	## ⚖️ License & Ownership
	This model weights are a sovereign asset owned by programmerGodbyte.
	Generated via [Smolify.ai](https://smolify.ai).

	[<img src="https://smolify.ai/smolify.gif" width="100"/>](https://smolify.ai)