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Model: yasserrmd/lfm2.5-1.5b-sdft Source: Original Platform
2026-07-18 08:13:12 +08:00
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+---
+base_model: LiquidAI/LFM2.5-1.2B-Instruct
+tags:
+- sdft
+- self-distillation
+- continual-learning
+- conversational
+language:
+- en
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# LFM2.5-1.2B-SDFT: Self-Distillation Fine-Tuned Model
+
+This model is a **Self-Distillation Fine-Tuned (SDFT)** version of [LiquidAI/LFM2.5-1.2B-Instruct](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct), trained using the methodology from the paper ["Self-Distillation Enables Continual Learning"](https://arxiv.org/abs/2601.19897).
+
+## Model Description
+
+- **Base Model:** LiquidAI/LFM2.5-1.2B-Instruct
+- **Training Method:** Self-Distillation Fine-Tuning (SDFT)
+- **Training Data:** ~5K samples from OpenAssistant dataset
+- **Training Hardware:** Single NVIDIA A100 GPU
+- **Parameters:** LoRA rank=8, alpha=16, targeting q_proj and v_proj
+
+### What is SDFT?
+
+Self-Distillation Fine-Tuning (SDFT) is a continual learning technique that:
+- Uses the model's **in-context learning** ability to create a demonstration-aware teacher
+- Generates training data **on-policy** from the student model
+- Minimizes KL divergence between student and demonstration-conditioned teacher
+- Enables learning new tasks while **reducing catastrophic forgetting**
+
+Key advantages:
+- ✅ Learns from demonstrations without explicit reward functions
+- ✅ Maintains prior knowledge while acquiring new skills
+- ✅ On-policy learning improves generalization
+- ✅ Efficient training with EMA teacher updates
+
+## Quick Start
+
+### Installation
+
+```bash
+pip install torch transformers peft accelerate bitsandbytes
+```
+
+### Basic Usage
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained("LiquidAI/LFM2.5-1.2B-Instruct")
+
+# Load model with quantization
+base_model = AutoModelForCausalLM.from_pretrained(
+    "yasserrmd/lfm2.5-1.5b-sdft",
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+
+model.eval()
+
+# Generate
+prompt = """<|im_start|>user
+Explain how photosynthesis works.
+<|im_end|>
+<|im_start|>assistant
+"""
+
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+
+# Use official LiquidAI parameters
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=256,
+    do_sample=True,
+    temperature=0.1,
+    top_k=50,
+    top_p=0.1,
+    repetition_penalty=1.05,
+    pad_token_id=tokenizer.pad_token_id
+)
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+### With Demonstration (In-Context Learning)
+
+```python
+prompt = """<|im_start|>user
+Explain how databases work.
+
+Here is an example response to guide you:
+
+Example: Databases store data in tables. You can query them to get information back.
+
+Now provide your own response following a similar approach:
+<|im_end|>
+<|im_start|>assistant
+"""
+
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=256,
+    do_sample=True,
+    temperature=0.1,
+    top_k=50,
+    top_p=0.1,
+    repetition_penalty=1.05
+)
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+## Training Details
+
+### Dataset
+- **Source:** OpenAssistant conversations
+- **Size:** ~5,000 query-demonstration pairs
+- **Preprocessing:** 
+  - Filtered demonstrations: 20-2048 characters
+  - Train/Val/Test split: 75%/10%/15%
+
+### Training Configuration
+
+```python
+# Model Architecture
+- Base: LiquidAI/LFM2.5-1.2B-Instruct
+- Quantization: 8-bit with bitsandbytes
+- LoRA: rank=8, alpha=16, dropout=0.05
+- Target modules: q_proj, v_proj
+
+# Training Parameters
+- Learning rate: 5e-6
+- Optimizer: AdamW (weight_decay=0.01)
+- Batch size: 1 (with gradient accumulation)
+- Gradient accumulation steps: 16
+- Epochs: 3
+- Max sequence length: 512
+- Max generation length: 128
+
+# SDFT-Specific
+- EMA alpha: 0.02
+- Temperature: 1.0
+- KL divergence: Analytic (full vocabulary)
+- On-policy generation: Yes
+```
+
+### Prompt Format (Teacher vs Student)
+
+**Student Prompt (query only):**
+```
+<|im_start|>user
+{query}
+<|im_end|>
+<|im_start|>assistant
+```
+
+**Teacher Prompt (query + demonstration):**
+```
+<|im_start|>user
+{query}
+
+Here is an example response to guide you:
+
+<|im_start|>assistant
+{demonstration}
+<|im_end|>
+
+<|im_start|>user
+Now provide your own response following a similar approach and reasoning:
+<|im_end|>
+<|im_start|>assistant
+```
+
+## Evaluation Results
+
+### Tested on Multiple Dimensions:
+
+| Category | Description | Performance |
+|----------|-------------|-------------|
+| **ICL Adaptation** | Following demonstration style | ✅ Good |
+| **Task Improvement** | Learning from examples | ✅ Good |
+| **Retention** | No catastrophic forgetting | ✅ ~80% |
+| **Polarity Control** | Following demo viewpoint | ⚠️ Moderate |
+
+### Key Findings:
+
+1. ✅ **Maintains Knowledge:** No significant forgetting on general tasks
+2. ✅ **Adapts to Demos:** Successfully follows demonstration styles
+3. ✅ **Improved Over Training:** Epoch 3 shows stable, coherent outputs
+4. ⚠️ **Model Size Limitation:** 1.2B parameters limits complex reasoning
+
+### Comparison to Base Model:
+
+- **With Demonstrations:** SDFT shows better style matching and task following
+- **Without Demonstrations:** Maintains base model capabilities
+- **Response Quality:** More consistent and focused outputs
+
+## Generation Parameters
+
+**⚠️ Important:** Use official LiquidAI parameters for best results:
+
+```python
+generation_config = {
+    "max_new_tokens": 256,
+    "do_sample": True,
+    "temperature": 0.1,        # Official LiquidAI recommendation
+    "top_k": 50,               # Official LiquidAI recommendation
+    "top_p": 0.1,              # Official LiquidAI recommendation
+    "repetition_penalty": 1.05 # Official LiquidAI recommendation
+}
+```
+
+These parameters are specifically tuned for LFM2.5 and provide:
+- Focused, factual responses
+- Minimal hallucinations
+- Consistent output quality
+
+## Limitations
+
+### Model Constraints:
+- **Size:** 1.2B parameters (smaller capacity than 7B+ models)
+- **Training Data:** 5K samples (vs paper's 20K+)
+- **Hardware:** Single A100 (vs paper's multi-GPU setup)
+- **Complexity:** Limited reasoning on very complex tasks
+
+### Known Issues:
+- May require proper ChatML formatting for best results
+- Performance degrades on tasks requiring deep technical knowledge
+- Smaller model size limits polarity control effectiveness
+
+### Appropriate Use Cases:
+- ✅ Conversational AI with example-guided responses
+- ✅ Task learning from demonstrations
+- ✅ Style-adaptive text generation
+- ✅ Educational/research purposes
+
+### Not Recommended For:
+- ❌ Production systems requiring 100% reliability
+- ❌ Tasks requiring strong reasoning (use 7B+ models)
+- ❌ Safety-critical applications
+- ❌ Tasks outside training distribution without demonstrations
+
+## Bias and Ethical Considerations
+
+- Inherits biases from base LFM2.5 model and OpenAssistant dataset
+- May generate inconsistent responses on controversial topics
+- Should not be used for medical, legal, or financial advice
+- Outputs should be reviewed by humans for critical applications
+
+## Citation
+
+If you use this model, please cite:
+
+**SDFT Paper:**
+```bibtex
+@article{shenfeld2026sdft,
+  title={Self-Distillation Enables Continual Learning},
+  author={Shenfeld, Idan and Damani, Mehul and H{\"u}botter, Jonas and Agrawal, Pulkit},
+  journal={arXiv preprint arXiv:2601.19897},
+  year={2026}
+}
+```
+
+**Base Model:**
+```bibtex
+@misc{lfm25,
+  title={LFM2.5: Liquid Foundation Models},
+  author={LiquidAI},
+  year={2024},
+  url={https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct}
+}
+```
+
+## Acknowledgments
+
+- **Paper:** ["Self-Distillation Enables Continual Learning"](https://arxiv.org/abs/2601.19897) by Shenfeld et al.
+- **Base Model:** [LiquidAI/LFM2.5-1.2B-Instruct](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct)
+- **Dataset:** OpenAssistant conversations
+- **Framework:** HuggingFace Transformers, PEFT, bitsandbytes
+
+## License
+
+This model is released under the Apache 2.0 license, following the base model's licensing.
+
+## Model Card Authors
+
+[Your Name/Organization]
+
+## Contact
+
+For questions or issues, please open an issue on the [model repository](https://huggingface.co/YOUR_USERNAME/lfm25-sdft).
+
+---
+
+## Additional Resources
+
+- 📄 [SDFT Paper](https://arxiv.org/abs/2601.19897)
+- 💻 [Training Code](https://github.com/YOUR_USERNAME/sdft-training)
+- 🤗 [Base Model](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct)
+- 📊 [Evaluation Results](link-to-detailed-results)
+
+## Version History
+
+- **v1.0** (2024-XX-XX): Initial release
+  - Trained on 5K OpenAssistant samples
+  - 3 epochs with gradient accumulation
+  - LoRA rank 8, alpha 16