Update README.md

README.md

@@ -15,7 +15,6 @@ For a deeper dive into the methods and results, check out our [blog post](https:
 
 ## Model Details
 
-
 ### Model Sources
 
 <!-- Provide the basic links for the model. -->
@@ -23,39 +22,6 @@ For a deeper dive into the methods and results, check out our [blog post](https:
 - **Repository:** [Model](https://huggingface.co/HF1BitLLM/Llama3-8B-1.58-100B-tokens)
 - **Paper:** [The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits](https://arxiv.org/abs/2402.17764)
 
-## 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
 
@@ -71,7 +37,7 @@ input_ids = tokenizer.encode(input_text, return_tensors="pt").cuda()
 output = model.generate(input_ids, max_length=10, do_sample=False)
 generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
 print(generated_text)
-
+```
 
 ## Training Details
 
@@ -79,112 +45,50 @@ print(generated_text)
 
 The model was trained on a subset of [FineWeb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu)
 
-[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. -->
+### Training Process
 
-[More Information Needed]
+1. **Starting Point**
+   - Best-performing checkpoint from the 10 billion token runs with a linear lambda scheduler
 
-#### Factors
+2. **Training Duration**
+   - Fine-tuned for an additional 45,000 steps
+   - Reached a total of 100 billion tokens
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+3. **Dataset**
+   - FineWeb-edu dataset
 
-[More Information Needed]
+4. **Batch Size**
+   - 2 million tokens per step
+   - Total per run: 45,000 steps * 2 million tokens = 90 billion tokens
+   - Combined with initial 10 billion tokens to reach 100 billion
 
-#### Metrics
+5. **Learning Rate Experiments**
+   - Tested various learning rates to find optimal setting, according the to experiments, the best performing peak lr is 1e-5
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+6. **Performance**
+   - Close to Llama3 8B on some metrics
+   - Behind Llama3 8B in overall average performance
 
-[More Information Needed]
+7. **Evaluation**
+   - Metrics included perplexity, MMLU scores, and other standard benchmarks
 
-### Results
+These extended training runs on 100 billion tokens pushed the boundaries of highly quantized models, bringing performance closer to half-precision models like Llama3.
 
-[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:**
-
-[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]
+## Evaluation
 
-## More Information [optional]
+The evaluation of the models is done on the nanotron checkpoints using LightEval : 
 
-[More Information Needed]
+![results](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/1.58llm_extreme_quantization/metrics_100B_table.png)
 
-## Model Card Authors [optional]
 
-[More Information Needed]
 
-## Model Card Contact
+## Citation
 
-[More Information Needed]
+```bash
+@misc{,
+      title={1.58-Bit LLM: A New Era of Extreme Quantization}, 
+      author={Mohamed Mekkouri and Marc Sun and Leandro von Werra and Thomas Wolf},
+      year={2024},
+}
+```