Instructions to use meta-llama/Llama-3.2-1B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use meta-llama/Llama-3.2-1B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="meta-llama/Llama-3.2-1B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B-Instruct")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B-Instruct")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use meta-llama/Llama-3.2-1B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "meta-llama/Llama-3.2-1B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "meta-llama/Llama-3.2-1B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/meta-llama/Llama-3.2-1B-Instruct

SGLang

How to use meta-llama/Llama-3.2-1B-Instruct 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 "meta-llama/Llama-3.2-1B-Instruct" \
    --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": "meta-llama/Llama-3.2-1B-Instruct",
		"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 "meta-llama/Llama-3.2-1B-Instruct" \
        --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": "meta-llama/Llama-3.2-1B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use meta-llama/Llama-3.2-1B-Instruct with Docker Model Runner:
```
docker model run hf.co/meta-llama/Llama-3.2-1B-Instruct
```

New architecture: TemporalMesh Transformer — dynamic kNN graph attention + per-token exit routing, 29.4 PPL at 48% compute

#180

by vigneshwar234 - opened 24 days ago

Discussion

vigneshwar234

24 days ago

TemporalMesh Transformer (TMT) — open-source, 120M params, state-of-the-art efficiency

Sharing a new transformer architecture for the community's feedback and comparison.

TMT achieves 29.4 PPL on WikiText-2 (−30.2% vs vanilla) at 48% relative compute — outperforming Mamba (31.8), RWKV (33.1), Longformer (39.6), and vanilla transformer (42.1) at ~120M parameters.

Five innovations unified in one forward pass

Mesh Attention — dynamic kNN graph (k=8) rebuilt per-layer from cosine similarity. O(S·k) vs O(S²). At S=1024: 128× fewer attention ops.
Temporal Decay Encoding — learned per-head multiplicative scalar post-softmax: ã_ij = α_ij × σ(w·|t_i−t_j|)
Adaptive Depth Routing — per-token exit gate, avg 5.76/12 layers used (52% compute saved)
Dual-Stream FFN — syntax + semantic parallel streams with sigmoid fusion gate
EMA Memory Anchors — 16 persistent fast-weight vectors (β=0.99), 32KB params

Results across 8 benchmarks

	WT-2 PPL↓	WT-103 PPL↓	LongBench↑	C4 PPL↓	Compute
Vanilla	42.1	51.3	41.2	38.4	100%
Longformer	39.6	47.2	49.8	36.1	62%
Mamba	31.8	38.4	51.3	30.1	55%
TMT	29.4	36.1	53.4	27.4	48%

Quick start

from tmt.model.config import TMTConfig
from tmt.model.model import TMTModel
model = TMTModel(TMTConfig(vocab_size=50257, d_model=512, n_heads=8, n_layers=12))
out = model(input_ids)
# out.logits, out.exit_masks, out.graph_edges, out.confidences

📄 Paper: https://zenodo.org/records/20287390 · DOI: 10.5281/zenodo.20287197
💻 Code (226 tests): https://github.com/vignesh2027/TemporalMesh-Transformer
🎮 Live Demo: https://huggingface.co/spaces/vigneshwar234/TemporalMesh-Transformer-Demo
🤗 Model: https://huggingface.co/vigneshwar234/TemporalMesh-Transformer

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