Instructions to use smolify/smolified-62f5cd93 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smolify/smolified-62f5cd93 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="smolify/smolified-62f5cd93")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("smolify/smolified-62f5cd93", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use smolify/smolified-62f5cd93 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "smolify/smolified-62f5cd93" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "smolify/smolified-62f5cd93", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/smolify/smolified-62f5cd93
- SGLang
How to use smolify/smolified-62f5cd93 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-62f5cd93" \ --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": "smolify/smolified-62f5cd93", "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 "smolify/smolified-62f5cd93" \ --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": "smolify/smolified-62f5cd93", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use smolify/smolified-62f5cd93 with Docker Model Runner:
docker model run hf.co/smolify/smolified-62f5cd93
π€ smolified-62f5cd93
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.
π¦ Asset Details
- Origin: Smolify Foundry (Job ID:
62f5cd93) - Architecture: DSLM-Micro (270M Parameter Class)
- Training Method: Proprietary Neural Distillation
- Optimization: 4-bit Quantized / FP16 Mixed
π Usage (Inference)
This model is compatible with standard inference backends like vLLM.
# Example: Running your Sovereign Model
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "smolify/smolified-62f5cd93"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
messages = [
{'role': 'system', 'content': '''You are a Named Entity Recognition model for Bengali-English text. Extract entities into JSON: {'PER': [], 'ORG': [], 'LOC': [], 'DATE': []}. Return JSON only.'''},
{'role': 'user', 'content': '''Amit ajke Victoria Memorial ghurte jabe bolche.'''}
]
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),
)
βοΈ License & Ownership
This model weights are a sovereign asset owned by the client. Generated via Smolify.ai.
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