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README.md

@@ -1,199 +1,272 @@
 ---
 library_name: transformers
-tags: []
+license: other
+license_name: lfm1.0
+license_link: LICENSE
+language:
+- en
+- ar
+- zh
+- fr
+- de
+- ja
+- ko
+- es
+- pt
+- it
+pipeline_tag: text-generation
+tags:
+- liquid
+- lfm2.5
+- edge
+- heretic
+- uncensored
+- decensored
+- abliterated
+- reproducible
+base_model: LiquidAI/LFM2.5-230M-Base
 ---
+# This is a decensored version of [LiquidAI/LFM2.5-230M](https://huggingface.co/LiquidAI/LFM2.5-230M), made using [Heretic](https://heretic-project.org) v1.4.0
+
+> [!TIP]
+> **This model is reproducible!**
+>
+> See the [README](reproduce/README.md) in the `reproduce` directory for more information.
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
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-[More Information Needed]
-
-**APA:**
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-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-[More Information Needed]
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-## More Information [optional]
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-[More Information Needed]
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-## Model Card Authors [optional]
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-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+## Abliteration parameters
+
+| Parameter | Value |
+| :-------- | :---: |
+| **direction_index** | 10.63 |
+| **attn.o_proj.max_weight** | 1.35 |
+| **attn.o_proj.max_weight_position** | 8.18 |
+| **attn.o_proj.min_weight** | 0.01 |
+| **attn.o_proj.min_weight_distance** | 7.49 |
+| **mlp.down_proj.max_weight** | 0.97 |
+| **mlp.down_proj.max_weight_position** | 8.14 |
+| **mlp.down_proj.min_weight** | 0.55 |
+| **mlp.down_proj.min_weight_distance** | 5.29 |
+
+## Performance
+
+| Metric | This model | Original model ([LiquidAI/LFM2.5-230M](https://huggingface.co/LiquidAI/LFM2.5-230M)) |
+| :----- | :--------: | :---------------------------: |
+| **KL divergence** | 0.0652 | 0 *(by definition)* |
+| **Refusals** | 4/100 | 95/100 |
+
+-----
+
+
+<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>
+
+# LFM2.5-230M
+
+LFM2.5 is a family of hybrid models designed for **on-device deployment**. It builds on the LFM2 architecture with extended pre-training and reinforcement learning.
+
+- **Our most compact model yet**: 230M parameters that punch above their weight, bringing real capability to the tightest memory and compute budgets.
+- **Fast edge inference**: Best throughput from low-cost CPUs to production GPUs, running at 213 tok/s decode speed on Galaxy S25 Ultra and 42 tok/s on a Raspberry Pi 5.
+- **Built for agentic tasks**: Distilled from LFM2.5-350M and refined with multi-stage reinforcement learning, making it well-suited for tool use and data extraction.
+
+Find more information about LFM2.5-230M in our [blog post](https://www.liquid.ai/blog/lfm2-5-230m).
+
+![lfm2_5_230m_benchmarks](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/4UpNxlgfKjfgT5ByIVph0.png)
+
+## 🗒️ Model Details
+
+| Model | Parameters | Description |
+|-------|------------|-------------|
+| [LFM2.5-230M-Base](https://huggingface.co/LiquidAI/LFM2.5-230M-Base) | 230M | Pre-trained base model for fine-tuning |
+| [**LFM2.5-230M**](https://huggingface.co/LiquidAI/LFM2.5-230M) | 230M | General-purpose instruction-tuned model |
+
+LFM2.5-230M is a general-purpose text-only model with the following features:
+
+- **Number of parameters**: 230M
+- **Number of layers**: 14 (8 double-gated LIV convolution blocks + 6 GQA blocks)
+- **Training budget**: 19T tokens
+- **Context length**: 32,768 tokens
+- **Vocabulary size**: 65,536
+- **Knowledge cutoff**: Mid-2024
+- **Languages**: English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish
+- **Generation parameters**:
+  - `temperature: 0.1`
+  - `top_k: 50`
+  - `repetition_penalty: 1.05`
+
+| Model | Description |
+|-------|-------------|
+| [**LFM2.5-230M**](https://huggingface.co/LiquidAI/LFM2.5-230M) | Original model checkpoint in native format. Best for fine-tuning or inference with Transformers, vLLM, and SGLang. |
+| [LFM2.5-230M-GGUF](https://huggingface.co/LiquidAI/LFM2.5-230M-GGUF) | Quantized format for llama.cpp and compatible tools. Optimized for edge inference and local deployment. |
+| [LFM2.5-230M-ONNX](https://huggingface.co/LiquidAI/LFM2.5-230M-ONNX) | ONNX Runtime format for cross-platform deployment. |
+| [LFM2.5-230M-MLX](https://huggingface.co/LiquidAI/LFM2.5-230M-MLX-8bit) | MLX format for Apple Silicon. Optimized for fast inference on Mac devices. |
+
+We recommend using it for data extraction and lightweight on-device agentic pipelines. It is not recommended for reasoning-heavy workloads such as advanced math, code generation, or creative writing.
+
+### 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.
+
+### 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|>
+```
+
+## 🏃 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/lm-studio">Link</a> | — |
+| [SGLang](https://github.com/sgl-project/sglang) | High-throughput production deployments with GPU. | <a href="https://lmsysorg.mintlify.app/cookbook/autoregressive/LiquidAI/LFM2.5">Link</a> | - </a> |
+
+
+Quick start with Transformers (compatible with `transformers>=5.0.0`):
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+
+model_id = "LiquidAI/LFM2.5-230M"
+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=1.05,
+    max_new_tokens=512,
+    streamer=streamer,
+)
+```
+
+## 🔧 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 ([Unsloth](https://github.com/unslothai/unsloth)) | GRPO with LoRA using Unsloth. | <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> | <a href="https://colab.research.google.com/drive/1mIikXFaGvcW4vXOZXLbVTxfBRw_XsXa5?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
+
+| Model | GPQA Diamond | MMLU-Pro | IFEval | IFBench | Multi-IF |
+|---|---|---|---|---|---|
+| **LFM2.5-230M** | 25.41 | 20.25 | 71.71 | 38.40 | 37.70 |
+| LFM2.5-350M | 30.64 | 20.01 | 76.96 | 40.69 | 44.92 |
+| LFM2-350M | 27.58 | 19.29 | 64.96 | 18.20 | 32.92 |
+| Granite 4.0-H-350M | 22.32 | 13.14 | 61.27 | 17.22 | 28.70 |
+| Granite 4.0-350M | 25.91 | 12.84 | 53.48 | 15.98 | 24.21 |
+| Qwen3.5-0.8B (Instruct) | 27.41 | 37.42 | 59.94 | 22.87 | 41.68 |
+| Gemma 3 1B IT | 23.89 | 14.04 | 63.49 | 20.33 | 44.25 |
+
+| Model | CaseReportBench | BFCLv3 | BFCLv4 | τ²-Bench Telecom | τ²-Bench Retail |
+|---|---|---|---|---|---|
+| **LFM2.5-230M** | 22.51 | 43.26 | 21.03 | 5.26 | 13.68 |
+| LFM2.5-350M | 32.45 | 44.11 | 21.86 | 18.86 | 17.84 |
+| LFM2-350M | 11.67 | 22.95 | 12.29 | 10.82 | 5.56 |
+| Granite 4.0-H-350M | 12.44 | 43.07 | 13.28 | 13.74 | 6.14 |
+| Granite 4.0-350M | 0.84 | 39.58 | 13.73 | 2.92 | 6.14 |
+| Qwen3.5-0.8B (Instruct) | 13.83 | 35.08 | 18.70 | 12.57 | 6.14 |
+| Gemma 3 1B IT | 2.28 | 16.61 | 7.17 | 9.36 | 6.43 |
+
+### CPU Inference
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/TCR-MfPtX3YTPvRzxWcG3.png)
+
+### GPU Inference
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/emlcz4gf2wendPhKQWEBN.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{liquidAI2026230M,
+  author = {Liquid AI},
+  title = {LFM2.5-230M: Built to Run Anywhere},
+  journal = {Liquid AI Blog},
+  year = {2026},
+  note = {www.liquid.ai/blog/lfm2-5-230m},
+}
+```
+```bibtex
+@article{liquidai2025lfm2,
+  title={LFM2 Technical Report},
+  author={Liquid AI},
+  journal={arXiv preprint arXiv:2511.23404},
+  year={2025}
+}
+```