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README.md

@@ -1,178 +1,209 @@
 ---
-library_name: peft
 base_model: LiquidAI/LFM2-2.6B-Exp
+library_name: peft
+pipeline_tag: text-generation
 tags:
+- base_model:adapter:LiquidAI/LFM2-2.6B-Exp
 - lora
 - sft
+- transformers
 - trl
-- code-agent
-- smolagents
-license: apache-2.0
-language:
-- en
-pipeline_tag: text-generation
 ---
 
-# LFM2-2.6B-CodeAgent-LoRA
+# 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. -->
+
+
+
+- **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
 
-A LoRA fine-tuned adapter for [LiquidAI/LFM2-2.6B-Exp](https://huggingface.co/LiquidAI/LFM2-2.6B-Exp) trained to follow the [smolagents](https://github.com/huggingface/smolagents) CodeAgent format.
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-## Model Description
+[More Information Needed]
 
-This adapter teaches LFM2-2.6B-Exp to respond in the structured Thought + Code format required by smolagents CodeAgent:
+### Recommendations
 
-```
-Thought: I need to calculate this.
-```python
-result = 2 + 2
-final_answer(result)
-```
-```
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
-### Key Features
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
-- **Base Model**: LiquidAI/LFM2-2.6B-Exp (2.6B parameter hybrid architecture with LIV convolution + GQA)
-- **Format Compliance**: 100% with minimal prompt
-- **Answer Accuracy**: 80% on evaluation tasks
-- **Adapter Size**: ~49MB (LoRA rank=8, alpha=16)
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
 
 ## Training Details
 
 ### Training Data
 
-- **130 successful CodeAgent trajectories** generated using Claude 3.5 Sonnet as the teacher model
-- Tasks include mathematical reasoning, string manipulation, and general problem-solving
-- Each trajectory demonstrates the Thought -> Code -> Observation -> final_answer pattern
+<!-- 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
 
-### Training Configuration
 
-| Parameter | Value |
-|-----------|-------|
-| LoRA Rank | 8 |
-| LoRA Alpha | 16 |
-| Target Modules | q_proj, v_proj |
-| Trainable Parameters | 12.2M (0.47% of base) |
-| Training Steps | 30 |
-| Learning Rate | 2e-4 |
-| Batch Size | 4 |
-| Max Sequence Length | 2048 |
-| Hardware | NVIDIA RTX 3090 (24GB) |
-| Training Time | ~5.5 hours |
 
-### Training Framework
+## Model Examination [optional]
 
-- [TRL](https://github.com/huggingface/trl) SFTTrainer
-- [PEFT](https://github.com/huggingface/peft) for LoRA
+<!-- Relevant interpretability work for the model goes here -->
 
-## Evaluation Results
+[More Information Needed]
 
-### Prompt Mode Comparison
+## Environmental Impact
 
-| Prompt Mode | Format Compliance | Answer Accuracy |
-|-------------|-------------------|-----------------|
-| **Minimal** | 100% | 80% |
-| Default | 80% | 80% |
-| None | 0% | 0% |
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
 
-The model performs best with the **minimal prompt** (~95 tokens), demonstrating successful prompt distillation.
+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).
 
-### Minimal Prompt Template
+- **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]
 
-```text
-You are a CodeAgent that solves tasks by writing and executing Python code.
+## Technical Specifications [optional]
 
-Always respond with Thought + Python code block. Example:
+### Model Architecture and Objective
 
-Thought: I need to calculate this.
-```python
-result = 2 + 2
-final_answer(result)
-```
+[More Information Needed]
 
-Call final_answer(result) when done. Now Begin!
-```
+### Compute Infrastructure
 
-## Usage
+[More Information Needed]
 
-### With PEFT
+#### Hardware
 
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from peft import PeftModel
+[More Information Needed]
 
-# Load base model
-base_model = AutoModelForCausalLM.from_pretrained(
-    "LiquidAI/LFM2-2.6B-Exp",
-    device_map="auto",
-    torch_dtype="bfloat16",
-)
-tokenizer = AutoTokenizer.from_pretrained("LiquidAI/LFM2-2.6B-Exp")
+#### Software
 
-# Load LoRA adapter
-model = PeftModel.from_pretrained(base_model, "krzysztofwos/LFM2-2.6B-CodeAgent-LoRA")
+[More Information Needed]
 
-# Generate
-messages = [{"role": "user", "content": "What is 15 * 23?"}]
-prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+## Citation [optional]
 
-outputs = model.generate(
-    **inputs,
-    max_new_tokens=512,
-    temperature=0.3,
-    min_p=0.15,
-)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-```
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
-### With smolagents
+**BibTeX:**
 
-```python
-from smolagents import CodeAgent, FinalAnswerTool, TransformersModel
+[More Information Needed]
 
-model = TransformersModel(
-    model_id="LiquidAI/LFM2-2.6B-Exp",
-    peft_model="krzysztofwos/LFM2-2.6B-CodeAgent-LoRA",
-)
+**APA:**
 
-agent = CodeAgent(
-    tools=[FinalAnswerTool()],
-    model=model,
-)
+[More Information Needed]
 
-result = agent.run("What is 15 * 23?")
-print(result)
-```
+## Glossary [optional]
 
-## Intended Use
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
 
-- Code-assisted problem solving
-- Mathematical reasoning tasks
-- Automated code generation following structured formats
-- Research into prompt distillation and small model fine-tuning
+[More Information Needed]
 
-## Limitations
+## More Information [optional]
 
-- **Requires specific prompt format**: Works best with minimal prompt template
-- **Limited reasoning depth**: 2.6B parameter hybrid architecture with LIV convolution + GQA model has constrained reasoning capabilities compared to larger models
-- **English only**: Trained on English-language tasks
+[More Information Needed]
 
-## Citation
+## Model Card Authors [optional]
 
-If you use this model, please cite:
+[More Information Needed]
 
-```bibtex
-@misc{lfm2_2.6b_codeagent_lora,
-  author = {krzysztofwos},
-  title = {LFM2-2.6B-CodeAgent-LoRA},
-  year = {2025},
-  publisher = {Hugging Face},
-  url = {https://huggingface.co/krzysztofwos/LFM2-2.6B-CodeAgent-LoRA}
-}
-```
+## Model Card Contact
 
-## Acknowledgments
+[More Information Needed]
+### Framework versions
 
-- [LiquidAI](https://www.liquid.ai/) for the LFM2-2.6B-Exp base model
-- [Hugging Face](https://huggingface.co/) for smolagents, TRL, and PEFT
-- Training performed as part of CodeAgent prompt distillation research
+- PEFT 0.18.0