Instructions to use Justbackup/LFM2.5-2.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Justbackup/LFM2.5-2.6B with Transformers:

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

pipe = pipeline("text-generation", model="Justbackup/LFM2.5-2.6B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Justbackup/LFM2.5-2.6B")
model = AutoModelForCausalLM.from_pretrained("Justbackup/LFM2.5-2.6B", 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 Justbackup/LFM2.5-2.6B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Justbackup/LFM2.5-2.6B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Justbackup/LFM2.5-2.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Justbackup/LFM2.5-2.6B

SGLang

How to use Justbackup/LFM2.5-2.6B 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 "Justbackup/LFM2.5-2.6B" \
    --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": "Justbackup/LFM2.5-2.6B",
		"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 "Justbackup/LFM2.5-2.6B" \
        --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": "Justbackup/LFM2.5-2.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Justbackup/LFM2.5-2.6B with Docker Model Runner:
```
docker model run hf.co/Justbackup/LFM2.5-2.6B
```

LFM2.5-2.6B / README.md

Justbackup

Duplicate from LiquidAI/LFM2.5-2.6B

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	---
	library_name: transformers
	license: other
	license_name: lfm1.0
	license_link: LICENSE
	language:
	- ar
	- zh
	- en
	- fr
	- de
	- hi
	- id
	- it
	- ja
	- ko
	- pl
	- pt
	- ru
	- es
	- th
	- vi
	pipeline_tag: text-generation
	tags:
	- liquid
	- lfm2.5
	- edge
	base_model: LiquidAI/LFM2.5-2.6B-Base
	---

	<div align="center">
	<img
	src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png"
	alt="Liquid AI"
	style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;"
	/>
	<div style="display: flex; justify-content: center; gap: 0.5em; margin-bottom: 1em;">
	<a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> •
	<a href="https://docs.liquid.ai/lfm/getting-started/welcome"><strong>Docs</strong></a> •
	<a href="https://leap.liquid.ai/"><strong>LEAP</strong></a> •
	<a href="https://discord.com/invite/liquid-ai"><strong>Discord</strong></a>
	</div>
	</div>

	# LFM2.5-2.6B

	LFM2.5-2.6B is part of LFM2.5, a family of hybrid models designed for on-device deployment. It builds on the LFM2 architecture with a 128K context window and agentic post-training.

	- Best-in-class agent: Competitive with models 4x larger on tool use, instruction following, and multi-step agentic tasks.
	- Agentic reinforcement learning: Trained inside the most popular agentic harnesses to improve compatibility.
	- Efficient inference: 220 tok/s on an Apple M5 Max and 113 tok/s on an AMD Ryzen CPU, in under 2.5 GB of memory.

	Find more information about LFM2.5-2.6B in our [blog post](https://www.liquid.ai/blog/lfm2-5-2-6b).

	![](https://cdn-uploads.huggingface.co/production/uploads/644249b08443bce4c9890a0f/NAfUL2725b6KDUGf1KOd9.png)

	> [!NOTE]
	> 💻 Demos: Try LFM2.5-2.6B's agentic capabilities in a Hugging Face space without any setup:
	> [Research Agent in your browser](https://huggingface.co/spaces/LiquidAI/LFM2.5-2.6B-WebGPU): helps you research a specific question and generates a summary

	## 🗒️ Model Details

	\| Model \| Parameters \| Description \|
	\| -------------------------------------------------------------------- \| ---------- \| -------------------------------------- \|
	\| [LFM2.5-2.6B-Base](https://huggingface.co/LiquidAI/LFM2.5-2.6B-Base) \| 2.6B \| Pre-trained base model for fine-tuning \|
	\| [LFM2.5-2.6B](https://huggingface.co/LiquidAI/LFM2.5-2.6B) \| 2.6B \| Post-trained for agentic workloads \|

	LFM2.5-2.6B is a general-purpose text-only model with the following features:

	- Total parameters: 2.69B
	- Number of layers: 30 (22 double-gated short convolution blocks + 8 GQA)
	- Training budget: 34 trillion tokens
	- Vocabulary size: 128,000
	- Context length: 131,072 tokens
	- Languages: English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish, Vietnamese, Thai, Indonesian, Hindi, Russian, Polish
	- Generation parameters:
	- `temperature: 0.1`
	- `top_k: 50`
	- `repetition_penalty: 1.1`

	\| Model \| Description \|
	\| ------------------------------------------------------------------------ \| -------------------------------------------------------------------------------------------------------------------------------------------- \|
	\| [LFM2.5-2.6B](https://huggingface.co/LiquidAI/LFM2.5-2.6B) \| Original model checkpoint in native format. Best for fine-tuning or inference with Transformers, vLLM, and SGLang. \|
	\| [LFM2.5-2.6B-GGUF](https://huggingface.co/LiquidAI/LFM2.5-2.6B-GGUF) \| Quantized format for llama.cpp and compatible tools. Optimized for CPU inference and local deployment with reduced memory usage. \|
	\| [LFM2.5-2.6B-ONNX](https://huggingface.co/LiquidAI/LFM2.5-2.6B-ONNX) \| ONNX Runtime format for cross-platform deployment. Enables hardware-accelerated inference across diverse environments (cloud, edge, mobile). \|
	\| [LFM2.5-2.6B-MLX](https://huggingface.co/LiquidAI/LFM2.5-2.6B-MLX) \| MLX format for Apple Silicon. Optimized for fast inference on Mac devices using the MLX framework. \|

	We recommend using it for agentic workloads, tool use, data extraction, RAG, and long-context workflows. It is not recommended for agentic coding and knowledge-heavy tasks.

	### Chat Template

	LFM2.5 uses a ChatML-like format. See the [Chat Template documentation](https://docs.liquid.ai/lfm/key-concepts/chat-template) for details. Example:

	```
	<\|startoftext\|><\|im_start\|>system
	You are a helpful assistant trained by Liquid AI.<\|im_end\|>
	<\|im_start\|>user
	What is C. elegans?<\|im_end\|>
	<\|im_start\|>assistant
	```

	You can use [`tokenizer.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_templating#using-applychattemplate) to format your messages automatically.

	> [!TIP]
	> 💡 Note: LFM2.5-2.6B is a pure reasoning model that always thinks before it answers. It adds a `<think>` tag directly in the [chat template](https://huggingface.co/LiquidAI/LFM2.5-2.6B/blob/main/chat_template.jinja#L124) when starting an assistant answer.

	### Tool Use

	LFM2.5 supports function calling in four steps:

	1. Function definition: Provide the list of tools as a JSON object in the system prompt, or use [`tokenizer.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_extras#passing-tools) with `tools=...`.
	2. Function call: By default, LFM2.5 writes Pythonic function calls (a Python list between `<\|tool_call_start\|>` and `<\|tool_call_end\|>` special tokens), as the assistant answer. You can override this behavior by asking the model to output JSON function calls in the system prompt.
	3. Function execution: Execute the call and return the result with the `tool` role.
	4. Final answer: LFM2.5 interprets the tool output and returns a plain-text answer addressing the original prompt.

	See the [Tool Use documentation](https://docs.liquid.ai/lfm/key-concepts/tool-use) for the full guide. Example:

	```
	<\|startoftext\|><\|im_start\|>system
	List of tools: [{"name": "get_candidate_status", "description": "Retrieves the current status of a candidate in the recruitment process", "parameters": {"type": "object", "properties": {"candidate_id": {"type": "string", "description": "Unique identifier for the candidate"}}, "required": ["candidate_id"]}}]<\|im_end\|>
	<\|im_start\|>user
	What is the current status of candidate ID 12345?<\|im_end\|>
	<\|im_start\|>assistant
	<\|tool_call_start\|>[get_candidate_status(candidate_id="12345")]<\|tool_call_end\|>Checking the current status of candidate ID 12345.<\|im_end\|>
	<\|im_start\|>tool
	[{"candidate_id": "12345", "status": "Interview Scheduled", "position": "Clinical Research Associate", "date": "2023-11-20"}]<\|im_end\|>
	<\|im_start\|>assistant
	The candidate with ID 12345 is currently in the "Interview Scheduled" stage for the position of Clinical Research Associate, with an interview date set for 2023-11-20.<\|im_end\|>
	```

	### Training

	LFM2.5-2.6B is pre-trained on ~34T tokens, with a mid-training phase that extends the context window to 128K. Post-training then turns the base model into an agent in four stages: supervised fine-tuning (two rounds), per-domain teacher specialization, multi-domain on-policy distillation, and agentic reinforcement learning.

	![](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/hCmGKgTRihAR9NfJlCVNN.png)

	In particular, agentic reinforcement learning allows us to directly train the model inside popular agentic harnesses. It exposes the model to their tools, system prompts, and interaction patterns, helping it work reliably across agent environments.

	![](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/LXqTXbw-lh0Yru5UWLMCq.png)

	## 🏃 Inference

	LFM2.5 is supported by many inference frameworks. See the [Inference documentation](https://docs.liquid.ai/lfm/inference/transformers) for the full list.

	\| Name \| Description \| Docs \| Notebook \|
	\|------\|-------------\|------\|:--------:\|
	\| [Transformers](https://github.com/huggingface/transformers) \| Simple inference with direct access to model internals. \| <a href="https://docs.liquid.ai/lfm/inference/transformers">Link</a> \| <a href="https://colab.research.google.com/drive/1_q3jQ6LtyiuPzFZv7Vw8xSfPU5FwkKZY?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> \|
	\| [vLLM](https://github.com/vllm-project/vllm) \| High-throughput production deployments with GPU. \| <a href="https://docs.liquid.ai/lfm/inference/vllm">Link</a> \| <a href="https://colab.research.google.com/drive/1VfyscuHP8A3we_YpnzuabYJzr5ju0Mit?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> \|
	\| [llama.cpp](https://github.com/ggml-org/llama.cpp) \| Cross-platform inference with CPU offloading. \| <a href="https://docs.liquid.ai/lfm/inference/llama-cpp">Link</a> \| <a href="https://colab.research.google.com/drive/1ohLl3w47OQZA4ELo46i5E4Z6oGWBAyo8?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> \|
	\| [MLX](https://github.com/ml-explore/mlx) \| Apple's machine learning framework optimized for Apple Silicon. \| <a href="https://docs.liquid.ai/lfm/inference/mlx">Link</a> \| — \|
	\| [LM Studio](https://lmstudio.ai/) \| Desktop application for running LLMs locally. \| <a href="https://docs.liquid.ai/lfm/inference/lmstudio">Link</a> \| — \|
	\| [SGLang](https://github.com/sgl-project/sglang) \| High-throughput production deployments with GPU. \| <a href="https://docs.sglang.ai/">Link</a> \| - \|

	## How to use

	LFM2.5-2.6B can be used for direct inference or as a backend for agentic workflows.

	### Quick start

	Get started with Transformers (compatible with `transformers>=5.0.0`):

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

	model_id = "LiquidAI/LFM2.5-2.6B"
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	device_map="auto",
	dtype="bfloat16",
	# attn_implementation="flash_attention_2" <- uncomment on compatible GPU
	)
	tokenizer = AutoTokenizer.from_pretrained(model_id)
	streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

	prompt = "What is C. elegans?"

	input_ids = tokenizer.apply_chat_template(
	[{"role": "user", "content": prompt}],
	add_generation_prompt=True,
	return_tensors="pt",
	tokenize=True,
	)["input_ids"].to(model.device)

	output = model.generate(
	input_ids,
	do_sample=True,
	temperature=0.1,
	top_k=50,
	repetition_penalty=1.1,
	max_new_tokens=512,
	streamer=streamer,
	)
	```

	### Agent Use

	LFM2.5-2.6B supports tool calling for agentic workflows.
	Serve it locally with any OpenAI-compatible backend (see [🏃 Inference](#🏃-inference), then configure your agent harness to connect to it.
	For full setup instructions including installation and additional options, see our [Agent Harnesses guide](https://docs.liquid.ai/examples/agent-harnesses).

	Note: The port depends on your serving backend — llama.cpp and MLX use `8080`, vLLM uses `8000`, SGLang uses `30000`, and LM Studio uses `1234`. Adjust the URLs below accordingly.

	#### Hermes

	Either use the interactive wizard or set it directly:

	```
	hermes config set model.provider custom
	hermes config set model.base_url http://localhost:8080/v1
	hermes config set model.default LFM2.5-2.6B
	hermes config set model.context_length 131072
	hermes config set model.api_mode chat_completions
	hermes config set agent.tool_use_enforcement true
	```

	#### OpenClaw

	Add to your config to `models.providers`:

	```
	local: {
	baseUrl: "http://localhost:8080/v1",
	apiKey: "sk-local",
	api: "openai-completions",
	models: [{
	id: "LFM2.5-2.6B",
	name: "LFM2.5-2.6B",
	contextWindow: 131072,
	maxTokens: 8192,
	cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 }
	}]
	}
	```

	#### Pi

	Add to your config to `~/.pi/agent/models.json`:

	```
	{
	"providers": {
	"local": {
	"baseUrl": "http://localhost:8080/v1",
	"api": "openai-completions",
	"apiKey": "local",
	"models": [{ "id": "LFM2.5-2.6B" }]
	}
	}
	}
	```

	## 🔧 Fine-Tuning

	We recommend fine-tuning LFM2.5 for your specific use case to achieve the best results.

	\| Name \| Description \| Docs \| Notebook \|
	\|------\|-------------\|------\|----------\|
	\| CPT ([Unsloth](https://github.com/unslothai/unsloth)) \| Continued Pre-Training using Unsloth for text completion. \| <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> \| <a href="https://colab.research.google.com/drive/10fm7eNMezs-DSn36mF7vAsNYlOsx9YZO?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> \|
	\| CPT ([Unsloth](https://github.com/unslothai/unsloth)) \| Continued Pre-Training using Unsloth for translation. \| <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> \| <a href="https://colab.research.google.com/drive/1gaP8yTle2_v35Um8Gpu9239fqbU7UgY8?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> \|
	\| SFT ([Unsloth](https://github.com/unslothai/unsloth)) \| Supervised Fine-Tuning with LoRA using Unsloth. \| <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> \| <a href="https://colab.research.google.com/drive/1vGRg4ksRj__6OLvXkHhvji_Pamv801Ss?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> \|
	\| SFT ([TRL](https://github.com/huggingface/trl)) \| Supervised Fine-Tuning with LoRA using TRL. \| <a href="https://docs.liquid.ai/lfm/fine-tuning/trl">Link</a> \| <a href="https://colab.research.google.com/drive/1j5Hk_SyBb2soUsuhU0eIEA9GwLNRnElF?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> \|
	\| DPO ([TRL](https://github.com/huggingface/trl)) \| Direct Preference Optimization with LoRA using TRL. \| <a href="https://docs.liquid.ai/lfm/fine-tuning/trl">Link</a> \| <a href="https://colab.research.google.com/drive/1MQdsPxFHeZweGsNx4RH7Ia8lG8PiGE1t?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> \|
	\| GRPO ([TRL](https://github.com/huggingface/trl)) \| GRPO with LoRA using TRL. \| <a href="https://docs.liquid.ai/lfm/fine-tuning/trl">Link</a> \| <a href="https://colab.research.google.com/github/Liquid4All/cookbook/blob/main/finetuning/notebooks/grpo_for_verifiable_tasks.ipynb"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> \|

	## 📊 Performance

	### Benchmarks

	We compared LFM2.5-2.6B with relevant sub-10B models on a diverse suite of benchmarks.

	\| Benchmark \| LFM2.5-2.6B (2.6B) \| gemma-4-E2B-it (5.1B) \| gemma-4-E4B-it (8B) \| Qwen3.5-4B (4.7B) \| Qwen3.5-9B (9.7B) \|
	\|---\|---:\|---:\|---:\|---:\|---:\|
	\| AA-Omni-Public Index \| -29.50 \| -74.47 \| -49.03 \| -54.30 \| -50.43 \|
	\| AA-Omni-Public Acc \| 8.13 \| 6.37 \| 8.33 \| 17.63 \| 21.30 \|
	\| AA-Omni-Public Non-hallu \| 59.04 \| 13.67 \| 37.42 \| 12.66 \| 8.84 \|
	\| AIME25 \| 51.87 \| 26.33 \| 34.27 \| 49.33 \| 56.07 \|
	\| LiveCodeBenchv6 \| 59.41 \| 54.92 \| 63.77 \| 60.85 \| 69.86 \|
	\| IFBench \| 59.17 \| 34.08 \| 39.24 \| 48.40 \| 56.47 \|
	\| Multi-IF \| 80.07 \| 69.44 \| 77.35 \| 55.67 \| 62.55 \|
	\| IFStruct \| 85.49 \| 64.85 \| 76.65 \| 36.25 \| 78.50 \|
	\| BFCLv4 \| 56.88 \| 36.98 \| 46.39 \| 50.56 \| 60.13 \|
	\| ToolSandbox \| 77.83 \| 52.40 \| 65.00 \| 75.55 \| 76.44 \|
	\| τ³-Bench Banking \| 5.67 \| 3.35 \| 4.12 \| 5.45 \| 5.15 \|
	\| Claw-Eval average (EN) \| 62.85 \| 53.14 \| 58.02 \| 62.28 \| 66.53 \|
	\| PinchBench \| 68.22 \| 44.24 \| 55.09 \| 71.26 \| 71.45 \|
	\| BrowseComp+ (OpenClaw) \| 26.89 \| 8.31 \| 15.90 \| 24.46 \| 27.23 \|

	### CPU Inference

	Due to its efficient LFM2 architecture, LFM2.5-2.6B is the fastest model we tested, with decode speeds of 220 tokens/s on an M5 Max and 113 tokens/s on a Ryzen AI Max+ 395. At 30 tokens/s, it allows you to run capable agents even on a phone.

	![](https://cdn-uploads.huggingface.co/production/uploads/644249b08443bce4c9890a0f/YwLcMpzsq1b5SW1Gtdt1o.png)

	### GPU Inference

	LFM2.5-2.6B is the fastest model in its size class, reaching almost 15K output tokens per second at high concurrency, roughly 1.3B tokens per day on a single H100.

	![](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/6EHzZzduUg1bISqkQuHZ6.png)

	## 📬 Contact

	- Got questions or want to connect? [Join our Discord community](https://discord.com/invite/liquid-ai)
	- If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).

	## Citation

	```bibtex
	@article{liquidAI202626B,
	author = {Liquid AI},
	title = {LFM2.5-2.6B: Agents Everywhere},
	journal = {Liquid AI Blog},
	year = {2026},
	note = {www.liquid.ai/blog/lfm2-5-2-6b},
	}
	```

	```bibtex
	@article{liquidai2025lfm2,
	title = {LFM2 Technical Report},
	author = {Liquid AI},
	journal = {arXiv preprint arXiv:2511.23404},
	year = {2025}
	}
	```