Update README.md

README.md

@@ -1,199 +1,227 @@
 ---
-library_name: transformers
-tags: []
+language:
+- en
+tags:
+- falcon3-Mamba-Instruct
+base_model:
+- tiiuae/Falcon3-Mamba-7B-Base
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# Falcon3-Mamba-7B-Instruct
 
+**Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B.
 
+This repository contains the **Falcon3-Mamba-7B-Instruct**. It achieves ,compared to similar SSM-based models of the same size, state of art results (at release's time) on reasoning, language understanding, instruction following, code and mathematics tasks.
+Falcon3-Mamba-7B-Instruct supports a context length up to 32K and 1 language (english).
 
 ## 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. -->
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
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-
-### Results
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-
-#### 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
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-#### Hardware
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-#### Software
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-## Citation [optional]
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-<!-- 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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-**APA:**
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-## 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 [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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+- Architecture(same as Falcon-Mamba-7b)
+  - Mamba1 based causal decoder only architecture trained on a causal language modeling task (i.e., predict the next token).
+  - 64 decoder blocks
+  - width: 4096
+  - state_size: 16 
+  - 32k context length
+  - 65k vocab size
+- Pretrained on 7 Teratokens of datasets comprising of web, code, STEM and high quality data using 2048 H100 GPU chips
+- Postrained on 1.2 million samples of STEM, conversations, code, and safety.
+- Developed by [Technology Innovation Institute](https://www.tii.ae)
+- License: TII Falcon-LLM License 2.0
+- Model Release Date: December 2024
+
+
+## Getting started
+
+<details>
+<summary> Click to expand </summary>
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "tiiuae/Falcon3-Mamba-7B-Instruct"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "How many hours in one day?"
+messages = [
+    {"role": "system", "content": "You are a helpful friendly assistant Falcon3 from TII, try to follow instructions as much as possible."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=1024
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
+</details>
+
+<br>
+
+# Benchmarks
+We report in the following table our internal pipeline benchmarks:
+
+<table border="1" style="width: 100%; text-align: center; border-collapse: collapse;">
+    <colgroup>
+        <col style="width: 10%;">
+        <col style="width: 10%;">
+        <col style="width: 7%;">
+        <col style="width: 7%;">
+        <col style="width: 7%;">
+        <col style="width: 7%;">
+        <col style="background-color: rgba(80, 15, 213, 0.5); width: 7%;">
+    </colgroup>
+    <thead>
+        <tr>
+            <th>Category</th>
+            <th>Benchmark</th>
+            <th>Zamba2-7B-instruct</th>
+            <th>Jamba-1.5-Mini-instruct</th>
+            <th>falcon-mamba-7b-instruct</th>
+            <th>Llama-3.1-8B-Instruct</th>
+            <th>Qwen2-7B-Instruct</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td rowspan="3">General</td>
+            <td>MMLU (5-shot)</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</68.5%>
+            <td>-</td>
+        </tr>
+        <tr>
+            <td>MMLU-PRO (5-shot)</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</29.6%>
+            <td>-</td>
+        </tr>
+        <tr>
+            <td>IFEval</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</78.6%>
+            <td>-</td>
+        </tr>
+        <tr>
+            <td rowspan="2">Math</td>
+            <td>GSM8K (5-shot)</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+        </tr>
+        <tr>
+            <td>MATH(4-shot)</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+        </tr>
+        <tr>
+            <td rowspan="4">Reasoning</td>
+            <td>Arc Challenge (25-shot)</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+        </tr>
+        <tr>
+            <td>GPQA (0-shot)</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</2.4%>
+            <td>-</td>
+        </tr>
+        <tr>
+            <td>MUSR (0-shot)</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</8.4%>
+            <td>-</td>
+        </tr>
+        <tr>
+            <td>BBH (3-shot)</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</29.9%>
+            <td>-</td>
+        </tr>
+        <tr>
+            <td rowspan="4">CommonSense Understanding</td>
+            <td>PIQA (0-shot)</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+        </tr>
+        <tr>
+            <td>SciQ (0-shot)</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+        </tr>
+        <tr>
+            <td>Winogrande (0-shot)</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+        </tr>
+        <tr>
+            <td>OpenbookQA (0-shot)</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+        </tr>
+    </tbody>
+</table>
+
+
+# Citation
+If Falcon3 family were helpful to your work, feel free to give us a cite.
+
+```
+@misc{Falcon3,
+    title = {The Falcon 3 family of Open Models},
+    author = {TII Team},
+    month = {December},
+    year = {2024}
+}
+```