Instructions to use xv0y5ncu/SmolLM2-135M-Instruct-GLQ-4bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xv0y5ncu/SmolLM2-135M-Instruct-GLQ-4bpw with Transformers:

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

pipe = pipeline("text-generation", model="xv0y5ncu/SmolLM2-135M-Instruct-GLQ-4bpw")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("xv0y5ncu/SmolLM2-135M-Instruct-GLQ-4bpw")
model = AutoModelForCausalLM.from_pretrained("xv0y5ncu/SmolLM2-135M-Instruct-GLQ-4bpw", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use xv0y5ncu/SmolLM2-135M-Instruct-GLQ-4bpw with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xv0y5ncu/SmolLM2-135M-Instruct-GLQ-4bpw"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xv0y5ncu/SmolLM2-135M-Instruct-GLQ-4bpw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/xv0y5ncu/SmolLM2-135M-Instruct-GLQ-4bpw

SGLang

How to use xv0y5ncu/SmolLM2-135M-Instruct-GLQ-4bpw 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 "xv0y5ncu/SmolLM2-135M-Instruct-GLQ-4bpw" \
    --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": "xv0y5ncu/SmolLM2-135M-Instruct-GLQ-4bpw",
		"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 "xv0y5ncu/SmolLM2-135M-Instruct-GLQ-4bpw" \
        --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": "xv0y5ncu/SmolLM2-135M-Instruct-GLQ-4bpw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use xv0y5ncu/SmolLM2-135M-Instruct-GLQ-4bpw with Docker Model Runner:
```
docker model run hf.co/xv0y5ncu/SmolLM2-135M-Instruct-GLQ-4bpw
```

SmolLM2-135M-Instruct-GLQ-4bpw / README.md

xv0y5ncu

card: point at SmolLM2-135M-Instruct-GLQ-block-diagonal-4bpw, which supersedes this build

6423afd verified 15 days ago

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metadata

license: apache-2.0
base_model: HuggingFaceTB/SmolLM2-135M-Instruct
tags:
  - glq
  - quantized
  - e8-lattice
library_name: transformers
pipeline_tag: text-generation

SmolLM2-135M-Instruct GLQ 4bpw

Superseded by xv0y5ncu/SmolLM2-135M-Instruct-GLQ-block-diagonal-4bpw. this build predates block-diagonal RHT, so its effective rate is ~10.07 bpw, not 4.0 — the padding was stored as real bits. The replacement is the same nominal rate with no waste.
vllm serve xv0y5ncu/SmolLM2-135M-Instruct-GLQ-block-diagonal-4bpw --quantization glq
These weights remain available and unchanged, so existing references keep working.

SmolLM2-135M-Instruct quantized using GLQ (Golay-Leech Quantization).

Note on effective bpw: This model was quantized with power-of-2 FHT padding. Effective storage is ~6.4 bpw due to dimensional padding (hidden_size=576 padded to 1024).

Usage

pip install glq

import glq.hf_integration
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "xv0y5ncu/SmolLM2-135M-Instruct-GLQ-4bpw",
    device_map="cuda",
    dtype="float16",
)
tokenizer = AutoTokenizer.from_pretrained("xv0y5ncu/SmolLM2-135M-Instruct-GLQ-4bpw")

inputs = tokenizer("The capital of France is", return_tensors="pt").to("cuda")
output = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Requirements

transformers >= 5.0
torch >= 2.0
glq >= 0.2.8 (pip install glq)

License

Apache 2.0

🔗 GLQ on GitHub: https://github.com/cnygaard/glq — if you like it, a ⭐ is appreciated.