Instructions to use QuantLLM/SmolLM2-135M-QuantLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantLLM/SmolLM2-135M-QuantLLM with Transformers:

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

pipe = pipeline("text-generation", model="QuantLLM/SmolLM2-135M-QuantLLM", device_map="auto")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("QuantLLM/SmolLM2-135M-QuantLLM")
model = AutoModelForCausalLM.from_pretrained("QuantLLM/SmolLM2-135M-QuantLLM", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use QuantLLM/SmolLM2-135M-QuantLLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantLLM/SmolLM2-135M-QuantLLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantLLM/SmolLM2-135M-QuantLLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/QuantLLM/SmolLM2-135M-QuantLLM

SGLang

How to use QuantLLM/SmolLM2-135M-QuantLLM 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 "QuantLLM/SmolLM2-135M-QuantLLM" \
    --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": "QuantLLM/SmolLM2-135M-QuantLLM",
		"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 "QuantLLM/SmolLM2-135M-QuantLLM" \
        --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": "QuantLLM/SmolLM2-135M-QuantLLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use QuantLLM/SmolLM2-135M-QuantLLM with Docker Model Runner:
```
docker model run hf.co/QuantLLM/SmolLM2-135M-QuantLLM
```

codewithdark commited on Apr 29

Commit

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+---
+license: apache-2.0
+base_model: HuggingFaceTB/SmolLM2-135M
+library_name: transformers
+language:
+  - en
+tags:
+  - quantllm
+  - transformers
+  - safetensors
+pipeline_tag: text-generation
+---
+<div align="center">
+# 🤗 SmolLM2-135M-QuantLLM
+**HuggingFaceTB/SmolLM2-135M** converted to **SAFETENSORS** format
+[![QuantLLM](https://img.shields.io/badge/🚀_Made_with-QuantLLM-orange?style=for-the-badge)](https://github.com/codewithdark-git/QuantLLM)
+[![Format](https://img.shields.io/badge/Format-SAFETENSORS-blue?style=for-the-badge)]()
+<a href="https://github.com/codewithdark-git/QuantLLM">⭐ Star QuantLLM on GitHub</a>
+</div>
+---
+## 📖 About This Model
+This model is **[HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M)** converted to **SafeTensors** format for use with HuggingFace Transformers and PyTorch.
+| Property | Value |
+|----------|-------|
+| **Base Model** | [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M) |
+| **Format** | SAFETENSORS |
+| **Quantization** | None (Full Precision) |
+| **License** | apache-2.0 |
+| **Created With** | [QuantLLM](https://github.com/codewithdark-git/QuantLLM) |
+## 🚀 Quick Start
+### With Transformers
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+# Load model and tokenizer
+model = AutoModelForCausalLM.from_pretrained("codewithdark/SmolLM2-135M-QuantLLM")
+tokenizer = AutoTokenizer.from_pretrained("codewithdark/SmolLM2-135M-QuantLLM")
+# Generate text
+inputs = tokenizer("Once upon a time", return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=100, do_sample=True)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+### With QuantLLM
+```python
+from quantllm import TurboModel
+# Load with automatic optimization
+model = TurboModel.from_pretrained("codewithdark/SmolLM2-135M-QuantLLM")
+# Generate
+response = model.generate("Write a poem about coding")
+print(response)
+```
+### Requirements
+```bash
+pip install transformers torch
+```
+## 📊 Model Details
+| Property | Value |
+|----------|-------|
+| **Original Model** | [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M) |
+| **Format** | SAFETENSORS |
+| **Quantization** | Full Precision |
+| **License** | `apache-2.0` |
+| **Export Date** | 2026-04-29 |
+| **Exported By** | [QuantLLM v2.1](https://github.com/codewithdark-git/QuantLLM) |
+---
+## 🚀 Created with QuantLLM
+<div align="center">
+[![QuantLLM](https://img.shields.io/badge/🚀_QuantLLM-Ultra--fast_LLM_Quantization-orange?style=for-the-badge)](https://github.com/codewithdark-git/QuantLLM)
+**Convert any model to GGUF, ONNX, or MLX in one line!**
+```python
+from quantllm import turbo
+# Load any HuggingFace model
+model = turbo("HuggingFaceTB/SmolLM2-135M")
+# Export to any format
+model.export("safetensors", quantization="Q4_K_M")
+# Push to HuggingFace
+model.push("your-repo", format="safetensors")
+```
+<a href="https://github.com/codewithdark-git/QuantLLM">
+  <img src="https://img.shields.io/github/stars/codewithdark-git/QuantLLM?style=social" alt="GitHub Stars">
+</a>
+**[📚 Documentation](https://github.com/codewithdark-git/QuantLLM#readme)** ·
+**[🐛 Report Issue](https://github.com/codewithdark-git/QuantLLM/issues)** ·
+**[💡 Request Feature](https://github.com/codewithdark-git/QuantLLM/issues)**
+</div>
+## 📊 Export Details
+Exported with [QuantLLM](https://github.com/codewithdark-git/QuantLLM) from `HuggingFaceTB/SmolLM2-135M` (134.5M params).
+| Property | Value |
+|----------|-------|
+| **Format** | SafeTensors |
+| **Size** | 541.6 MB |
+| **Parameters** | 134.5M |
+| **Dtype** | float32 |
+### How to use