Instructions to use tiny-random/lfm3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiny-random/lfm3 with Transformers:

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

pipe = pipeline("text-generation", model="tiny-random/lfm3", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiny-random/lfm3", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("tiny-random/lfm3", trust_remote_code=True, 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tiny-random/lfm3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tiny-random/lfm3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiny-random/lfm3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tiny-random/lfm3

SGLang

How to use tiny-random/lfm3 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 "tiny-random/lfm3" \
    --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": "tiny-random/lfm3",
		"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 "tiny-random/lfm3" \
        --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": "tiny-random/lfm3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tiny-random/lfm3 with Docker Model Runner:
```
docker model run hf.co/tiny-random/lfm3
```

lfm3 / README.md

yujiepan

Upload folder using huggingface_hub

f0e7fe0 verified 7 days ago

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---
library_name: transformers
pipeline_tag: text-generation
base_model:
- LiquidAI/LFM3-1.7B
---
This is a randomly initialized tiny model based on [LiquidAI/LFM3-1.7B](https://huggingface.co/LiquidAI/LFM3-1.7B) for testing and debugging.

| Feature | Source | This repo |
|---|---|---|
| Checkpoint tensor elements | 1.73B BF16 | 8.62M BF16 |
| Safetensors weight files | 3.46 GB (model.safetensors) | 17.26 MB (model.safetensors) |
| Layers / layer-type cycle | 24 / two-conv prefix, then full/conv/sliding/conv | 10 / same prefix and two complete cycles |
| Hidden width / MLP width | 2,048 / 7,680 | 64 / 128 |
| Q heads / KV heads / head_dim | 16 / 4 / 128 | 4 / 1 / 32 |
| Short-conv kernel / cache | 7 / 7 | Same |
| RoPE | full 0.5 partial, theta 1e6; sliding theta 1e4 | Same |
| Sandwich norms / QK RMSNorm / sigmoid head gate | present | Same |
| Vocabulary rows / embedding tying | 128,000 / tied | Same |
| Maximum context length | 262,144 | 128,000 |
| Stored precision | BF16 | Same |
Residual width 64 and MLP width 128 follow [tiny-random/lfm2](https://huggingface.co/tiny-random/lfm2). The pinned upstream implementation is included for Transformers loading. This repo provides a Transformers example; no serving-backend validation is claimed.
| File path | Size |
|------|------|
| model.safetensors | 17.3MB |
### Example usage
#### Transformers (✅ verified)
Load the included pinned upstream implementation with `trust_remote_code=True`.
```python
def run_transformers(model_id: str):
    import torch
    from transformers import AutoModelForCausalLM, AutoTokenizer
    model_id = str(model_id)
    tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
    model = (
        AutoModelForCausalLM.from_pretrained(
            model_id, trust_remote_code=True, dtype=torch.bfloat16, attn_implementation="sdpa"
        )
        .eval()
        .cuda()
    )
    inputs = tokenizer.apply_chat_template(
        [{"role": "user", "content": "What is C. elegans?"}],
        add_generation_prompt=True,
        return_tensors="pt",
        return_dict=True,
        enable_thinking=False,
    ).to(model.device)
    with torch.no_grad():
        logits = model(**inputs).logits
        assert torch.isfinite(logits).all()
        output = model.generate(**inputs, do_sample=False, max_new_tokens=32)
    print(tokenizer.decode(output[0, inputs["input_ids"].shape[1] :], skip_special_tokens=False))

if __name__ == "__main__":
    run_transformers(model_id="tiny-random/lfm3")
```
### Creating this repo

- Code: [.meta/create_model.py](.meta/create_model.py)
- Model structure: [.meta/model.log](.meta/model.log)
- Environment: [.meta/env.json](.meta/env.json)

### Change log

- 2026-10-30: Initial version.
- 2026-10-31: Resize to hidden width 64 and MLP width 128, following tiny-random/lfm2; use four Q heads and one KV head while preserving the LFM3 layer cadence.

*This repo is generated by GPT-6.1-Sol.*

	---
	library_name: transformers
	pipeline_tag: text-generation
	base_model:
	- LiquidAI/LFM3-1.7B
	---

	This is a randomly initialized tiny model based on [LiquidAI/LFM3-1.7B](https://huggingface.co/LiquidAI/LFM3-1.7B) for testing and debugging.

	\| Feature \| Source \| This repo \|
	\|---\|---\|---\|
	\| Checkpoint tensor elements \| 1.73B BF16 \| 8.62M BF16 \|
	\| Safetensors weight files \| 3.46 GB (model.safetensors) \| 17.26 MB (model.safetensors) \|
	\| Layers / layer-type cycle \| 24 / two-conv prefix, then full/conv/sliding/conv \| 10 / same prefix and two complete cycles \|
	\| Hidden width / MLP width \| 2,048 / 7,680 \| 64 / 128 \|
	\| Q heads / KV heads / head_dim \| 16 / 4 / 128 \| 4 / 1 / 32 \|
	\| Short-conv kernel / cache \| 7 / 7 \| Same \|
	\| RoPE \| full 0.5 partial, theta 1e6; sliding theta 1e4 \| Same \|
	\| Sandwich norms / QK RMSNorm / sigmoid head gate \| present \| Same \|
	\| Vocabulary rows / embedding tying \| 128,000 / tied \| Same \|
	\| Maximum context length \| 262,144 \| 128,000 \|
	\| Stored precision \| BF16 \| Same \|

	Residual width 64 and MLP width 128 follow [tiny-random/lfm2](https://huggingface.co/tiny-random/lfm2). The pinned upstream implementation is included for Transformers loading. This repo provides a Transformers example; no serving-backend validation is claimed.

	\| File path \| Size \|
	\|------\|------\|
	\| model.safetensors \| 17.3MB \|

	### Example usage

	#### Transformers (✅ verified)

	Load the included pinned upstream implementation with `trust_remote_code=True`.

	```python
	def run_transformers(model_id: str):
	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = str(model_id)
	tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
	model = (
	AutoModelForCausalLM.from_pretrained(
	model_id, trust_remote_code=True, dtype=torch.bfloat16, attn_implementation="sdpa"
	)
	.eval()
	.cuda()
	)
	inputs = tokenizer.apply_chat_template(
	[{"role": "user", "content": "What is C. elegans?"}],
	add_generation_prompt=True,
	return_tensors="pt",
	return_dict=True,
	enable_thinking=False,
	).to(model.device)
	with torch.no_grad():
	logits = model(**inputs).logits
	assert torch.isfinite(logits).all()
	output = model.generate(**inputs, do_sample=False, max_new_tokens=32)
	print(tokenizer.decode(output[0, inputs["input_ids"].shape[1] :], skip_special_tokens=False))


	if __name__ == "__main__":
	run_transformers(model_id="tiny-random/lfm3")
	```

	### Creating this repo

	- Code: [.meta/create_model.py](.meta/create_model.py)
	- Model structure: [.meta/model.log](.meta/model.log)
	- Environment: [.meta/env.json](.meta/env.json)

	### Change log

	- 2026-10-30: Initial version.
	- 2026-10-31: Resize to hidden width 64 and MLP width 128, following tiny-random/lfm2; use four Q heads and one KV head while preserving the LFM3 layer cadence.

	This repo is generated by GPT-6.1-Sol.