create readme

README.md

@@ -1,199 +1,117 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- text-generation
+- conversational
+datasets:
+- TIGER-Lab/WebInstructSub
+language:
+- en
+base_model:
+- HuggingFaceTB/SmolLM-360M-Instruct
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for TrelisLM-80M-SFT
 
+This model is a fine-tuned version of TrelisLM-80M, optimized for instruction following and conversational tasks using the WebInstructSub dataset.
 
 ## 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.
+TrelisLM-80M-SFT is an 80 million parameter language model derived from SmolLM-360M through pruning and distillation, and then fine-tuned on the WebInstructSub dataset for improved instruction following capabilities.
 
-- **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]
+- **Developed by:** Trelis AI
+- **Model type:** Causal Language Model
+- **Language(s):** English
 - **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Finetuned from model:** Trelis/80M-0.0090-cosmopedia
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** https://huggingface.co/Trelis/80M-2percent-corpus-SFT
 
 ## 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. -->
+This model is designed for instruction following and conversational tasks. It can be used for:
 
-[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]
+- Generating responses to user prompts or questions
+- Engaging in task-oriented dialogues
+- Assisting with general language understanding and generation tasks
 
 ### 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.
+This model should not be used for:
 
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+- Production systems without thorough testing and evaluation
+- Tasks requiring domain-specific expertise without additional fine-tuning
+- Any applications where errors could lead to harmful consequences
 
 ## 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. -->
+The model was fine-tuned on the TIGER-Lab/WebInstructSub dataset, which consists of instruction-response pairs. The training process used:
 
-[More Information Needed]
+- 50,000 initial rows for the main training phase
+- 10,000 additional rows for an annealing phase
+- 10,000 randomly selected rows for evaluation
 
 ### 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]
-
+- **Preprocessing:** The dataset was formatted into a conversational structure with user and assistant messages.
+- **Training type:** Supervised Fine-Tuning (SFT)
+- **Training regime:** BFloat16 mixed precision
 
 #### 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 -->
+- Batch size: 8
+- Gradient Accumulation steps: 4
+- Learning rate: 1e-3
+- Number of epochs: 1
+- Max sequence length: 2048
+- Warmup steps: 20
 
-#### Speeds, Sizes, Times [optional]
+The training used a custom learning rate scheduler with an initial constant phase followed by cosine annealing.
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+### Software and Hardware
 
-[More Information Needed]
+- **Software:** Transformers, TRL (Transformer Reinforcement Learning), Accelerate
+- **Hardware:** [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
+Evaluation was performed on a randomly selected subset of 10,000 rows from the WebInstructSub dataset.
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+### Metrics
 
 [More Information Needed]
 
-### Results
+## Limitations and Bias
 
-[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
+As this model is fine-tuned on the WebInstructSub dataset, it may inherit biases present in that dataset. Additionally, as a smaller language model, it may have limitations in handling complex or highly specialized tasks compared to larger models.
 
-[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:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
+### Recommendations
 
-## More Information [optional]
+- Thoroughly test the model's outputs before using it in any sensitive applications.
+- Be aware that the model's knowledge is limited to its training data and it may produce incorrect or biased information.
+- For critical applications, consider using this model in conjunction with other sources of information or larger, more comprehensive models.
 
-[More Information Needed]
+## How to Get Started with the Model
 
-## Model Card Authors [optional]
+You can use this model with the Transformers library:
 
-[More Information Needed]
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-## Model Card Contact
+model = AutoModelForCausalLM.from_pretrained("Trelis/80M-2percent-corpus-SFT")
+tokenizer = AutoTokenizer.from_pretrained("Trelis/80M-2percent-corpus-SFT")
 
-[More Information Needed]
+# Example usage
+input_text = "What is the capital of France?"
+input_ids = tokenizer.encode(input_text, return_tensors="pt")
+output = model.generate(input_ids, max_length=50)
+response = tokenizer.decode(output[0], skip_special_tokens=True)
+print(response)