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Model: unsloth/LFM2-2.6B-Exp-GGUF Source: Original Platform
2026-10-10 09:07:13 +08:00
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+---
+base_model:
+- LiquidAI/LFM2-2.6B-Exp
+library_name: transformers
+license: other
+license_name: lfm1.0
+license_link: https://huggingface.co/LiquidAI/LFM2-2.6B-Exp/blob/main/LICENSE
+language:
+- en
+- ar
+- zh
+- fr
+- de
+- ja
+- ko
+- es
+pipeline_tag: text-generation
+tags:
+- liquid
+- unsloth
+- lfm2
+- edge
+---
+> [!NOTE]
+>  Includes Unsloth **chat template fixes**! <br> For `llama.cpp`, use `--jinja`
+>
+
+<div>
+<p style="margin-top: 0;margin-bottom: 0;">
+    <em><a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0</a> achieves superior accuracy & outperforms other leading quants.</em>
+  </p>
+  <div style="display: flex; gap: 5px; align-items: center; ">
+    <a href="https://github.com/unslothai/unsloth/">
+      <img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
+    </a>
+    <a href="https://discord.gg/unsloth">
+      <img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
+    </a>
+    <a href="https://docs.unsloth.ai/">
+      <img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
+    </a>
+  </div>
+</div>
+
+
+<center>
+<div style="text-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>
+<div style="display: flex; justify-content: center; gap: 0.5em;">
+  <a href="https://playground.liquid.ai/chat">
+<a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> • <a href="https://docs.liquid.ai/lfm"><strong>Documentation</strong></a> • <a href="https://leap.liquid.ai/"><strong>LEAP</strong></a></a>
+</div>
+</center>
+
+# LFM2-2.6B-Exp
+
+LFM2-2.6B-Exp is an experimental checkpoint built on [LFM2-2.6B](https://huggingface.co/LiquidAI/LFM2-2.6B) using pure reinforcement learning.
+
+Specifically trained on instruction following, knowledge, and math, it delivers particularly strong performance compared to other 3B models. 
+In particular, its IFBench score surpasses DeepSeek R1-0528, a model 263 times larger.
+
+![LFM2.6B-Exp-White_v1](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/5q5J1GZ-gAfguC1qafr8S.png)
+
+Find more information about LFM2 in our [blog post](https://www.liquid.ai/blog/liquid-foundation-models-v2-our-second-series-of-generative-ai-models).
+
+## 📄 Model details
+
+Due to their small size, **we recommend fine-tuning LFM2 models on narrow use cases** to maximize performance. 
+They are particularly suited for agentic tasks, data extraction, RAG, creative writing, and multi-turn conversations. 
+However, we do not recommend using them for tasks that are knowledge-intensive or require programming skills.
+
+| Property            | [**LFM2-350M**](https://huggingface.co/LiquidAI/LFM2-350M) | [**LFM2-700M**](https://huggingface.co/LiquidAI/LFM2-700M) | [**LFM2-1.2B**](https://huggingface.co/LiquidAI/LFM2-1.2B) | [**LFM2-2.6B**](https://huggingface.co/LiquidAI/LFM2-2.6B) |
+| ------------------- | ----------------------------- | ----------------------------- | ----------------------------- | ----------------------------- |
+| **Parameters**      | 354,483,968                   | 742,489,344                   | 1,170,340,608                 | 2,569,272,320                 |
+| **Layers**          | 16 (10 conv + 6 attn)         | 16 (10 conv + 6 attn)         | 16 (10 conv + 6 attn)         | 30 (22 conv + 8 attn)         |
+| **Context length**  | 32,768 tokens                 | 32,768 tokens                 | 32,768 tokens                 | 32,768 tokens                 |
+| **Vocabulary size** | 65,536                        | 65,536                        | 65,536                        | 65,536                        |
+| **Precision**       | bfloat16                      | bfloat16                      | bfloat16                      | bfloat16                      |
+| **Training budget** | 10 trillion tokens            | 10 trillion tokens            | 10 trillion tokens            | 10 trillion tokens            |
+| **License**         | LFM Open License v1.0         | LFM Open License v1.0         | LFM Open License v1.0         | LFM Open License v1.0         
+
+**Supported languages**: English, Arabic, Chinese, French, German, Japanese, Korean, and Spanish.
+
+**Generation parameters**: We recommend the following parameters:
+* `temperature=0.3`
+* `min_p=0.15`
+* `repetition_penalty=1.05`
+
+**Chat template**: LFM2 uses a ChatML-like chat template as follows:
+
+```
+<|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
+It's a tiny nematode that lives in temperate soil environments.<|im_end|>
+```
+
+You can automatically apply it using the dedicated [`.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_templating#applychattemplate) function from Hugging Face transformers.
+
+**Tool use**: It consists of four main steps:
+1. **Function definition**: LFM2 takes JSON function definitions as input (JSON objects between `<|tool_list_start|>` and `<|tool_list_end|>` special tokens), usually in the system prompt
+2. **Function call**: LFM2 writes Pythonic function calls (a Python list between `<|tool_call_start|>` and `<|tool_call_end|>` special tokens), as the assistant answer.
+3. **Function execution**: The function call is executed and the result is returned (string between `<|tool_response_start|>` and `<|tool_response_end|>` special tokens), as a "tool" role.
+4. **Final answer**: LFM2 interprets the outcome of the function call to address the original user prompt in plain text.
+
+Here is a simple example of a conversation using tool use:
+
+```
+<|startoftext|><|im_start|>system
+List of tools: <|tool_list_start|>[{"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"]}}]<|tool_list_end|><|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
+<|tool_response_start|>[{"candidate_id": "12345", "status": "Interview Scheduled", "position": "Clinical Research Associate", "date": "2023-11-20"}]<|tool_response_end|><|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|>
+```
+
+You can directly pass tools as JSON schema or Python functions with `.apply_chat_template()` as shown in [this page](https://huggingface.co/docs/transformers/en/chat_extras) to automatically format the system prompt.
+
+**Architecture**: Hybrid model with multiplicative gates and short convolutions: 10 double-gated short-range LIV convolution blocks and 6 grouped query attention (GQA) blocks.
+
+**Pre-training mixture**: Approximately 75% English, 20% multilingual, and 5% code data sourced from the web and licensed materials.
+
+**Training approach**:
+* Very large-scale SFT on 50% downstream tasks, 50% general domains
+* Custom DPO with length normalization and semi-online datasets
+* Iterative model merging
+* Reinforcement learning with verifiable rewards
+
+## 🏃 How to run LFM2
+
+### 1. Transformers
+
+To run LFM2, you need to install Hugging Face [`transformers`](https://github.com/huggingface/transformers) v4.55 or a more recent version as follows:
+
+```bash
+pip install -U transformers
+```
+
+Here is an example of how to generate an answer with transformers in Python:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model and tokenizer
+model_id = "LiquidAI/LFM2-2.6B-Exp"
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype="bfloat16",
+#    attn_implementation="flash_attention_2" <- uncomment on compatible GPU
+)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+# Generate answer
+prompt = "What is C. elegans?"
+input_ids = tokenizer.apply_chat_template(
+    [{"role": "user", "content": prompt}],
+    add_generation_prompt=True,
+    return_tensors="pt",
+    tokenize=True,
+).to(model.device)
+
+output = model.generate(
+    input_ids,
+    do_sample=True,
+    temperature=0.3,
+    min_p=0.15,
+    repetition_penalty=1.05,
+    max_new_tokens=512,
+)
+
+print(tokenizer.decode(output[0], skip_special_tokens=False))
+
+# <|startoftext|><|im_start|>user
+# What is C. elegans?<|im_end|>
+# <|im_start|>assistant
+# C. elegans, also known as Caenorhabditis elegans, is a small, free-living
+# nematode worm (roundworm) that belongs to the phylum Nematoda.
+```
+
+You can directly run and test the model with this [Colab notebook](https://colab.research.google.com/drive/1_q3jQ6LtyiuPzFZv7Vw8xSfPU5FwkKZY?usp=sharing).
+
+### 2. vLLM
+
+You need to install [`vLLM`](https://github.com/vllm-project/vllm) v0.10.2 or a more recent version as follows:
+
+```bash
+uv pip install vllm==0.10.2 --extra-index-url https://wheels.vllm.ai/0.10.2/ --torch-backend=auto
+```
+
+Here is an example of how to use it for inference:
+
+```python
+from vllm import LLM, SamplingParams
+
+prompts = [
+    "What is C. elegans?",
+    "Say hi in JSON format",
+    "Define AI in Spanish"
+]
+sampling_params = SamplingParams(temperature=0.3, min_p=0.15, repetition_penalty=1.05)
+
+llm = LLM(model="LiquidAI/LFM2-2.6B-Exp")
+
+outputs = llm.generate(prompts, sampling_params)
+
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+```
+
+### 3. llama.cpp
+
+You can run LFM2 with llama.cpp using its [GGUF checkpoint](https://huggingface.co/LiquidAI/LFM2-2.6B-Exp-GGUF). Find more information in the model card.
+
+## 🔧 How to fine-tune LFM2
+
+We recommend fine-tuning LFM2 models on your use cases to maximize performance.
+
+| Notebook | Description | Link |
+|-------|------|------|
+| SFT (Unsloth) | Supervised Fine-Tuning (SFT) notebook with a LoRA adapter using Unsloth. | <a href="https://colab.research.google.com/drive/1HROdGaPFt1tATniBcos11-doVaH7kOI3?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+| SFT (TRL) | Supervised Fine-Tuning (SFT) notebook with a LoRA adapter using TRL. | <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) | Preference alignment with Direct Preference Optimization (DPO) using TRL. | <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> |
+
+## 📬 Contact
+
+If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).
+
+## Citation
+
+```
+@article{liquidai2025lfm2,
+ title={LFM2 Technical Report},
+ author={Liquid AI},
+ journal={arXiv preprint arXiv:2511.23404},
+ year={2025}
+}
+```
--- a/configuration.json
+++ b/configuration.json
@@ -0,0 +1 @@
+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
				`@@ -0,0 +1 @@`
				`{"framework": "pytorch", "task": "text-generation", "allow_remote": true}`