push model card

README.md

@@ -1,6 +1,7 @@
 ---
 language:
 - en
+- fr
 - es
 - pt
 tags:
@@ -11,127 +12,61 @@ license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
 ---
 
 
+# Falcon3-1B-Base
 
-#  Table of Contents
+**Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B parameters.
 
-0. [TL;DR](#TL;DR)
-1. [Model Details](#model-details)
-2. [Usage](#usage)
-3. [Training Details](#training-details)
-4. [Evaluation](#evaluation)
+This repository contains the **Falcon3-1B-Base**. It achieves strong results on reasoning, language understanding, instruction following, code and mathematics tasks.
+Falcon3-1B-Base supports 4 languages (English, French, Spanish, Portuguese) and a context length of up to 32K.
+It was pruned in terms of depth, width, number of heads, and embedding channels from a larger 3B Falcon model, and was efficiently trained on only 80 GT using a knowledge distillation objective.
 
+⚠️ **This is a raw, pretrained model, which should be further finetuned using SFT, RLHF, continued pretraining, etc. for most use cases.** 
 
-# TL;DR
+## Model Details
+- Architecture
+  - Transformer-based causal decoder-only architecture
+  - 22 decoder blocks
+  - Grouped Query Attention (GQA) for faster inference: 8 query heads and 4 key-value heads
+  - Wider head dimension: 256
+  - High RoPE value to support long context understanding: 1000042
+  - Uses SwiGLU and RMSNorm
+  - 32K context length
+  - 131K vocab size
+- Pruned and healed using larger Falcon models (3B and 7B respectively) on only 80 Gigatokens of datasets comprising of web, code, STEM, high quality and mutlilingual data using 256 H100 GPU chips
+- Supports EN, FR, ES, PT
+- Developed by [Technology Innovation Institute](https://www.tii.ae)
+- License: TII Falcon-LLM License 2.0
+- Model Release Date: December 2024
 
-# Model Details
 
-⚠️ **This is a raw, pretrained model, which should be further finetuned for most usecases.** 
-
-## Model Description
-
-- **Developed by:** [https://www.tii.ae](https://www.tii.ae)
-- **Model type:** Causal decoder-only
-- **Architecture:** Transformer-base
-- **Language(s) (NLP):** Mainly English
-- **License:** TII Falcon-LLM License 2.0
-
-<br>
-
-# Usage
-
-Find below some example scripts on how to use the model in `transformers` (Make sure to have the latest transformers, or the one built from source):
-
-## Using the Pytorch model with 🤗 transformers
-
-### Running the model on a CPU
-
-<details>
-<summary> Click to expand </summary>
-
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-tokenizer = AutoTokenizer.from_pretrained("tiiuae/Falcon3-7B-Base")
-model = AutoModelForCausalLM.from_pretrained("tiiuae/Falcon3-7B-Base")
-
-input_text = "Question: How many hours in one day? Answer: "
-input_ids = tokenizer(input_text, return_tensors="pt").input_ids
-
-outputs = model.generate(input_ids)
-print(tokenizer.decode(outputs[0]))
-```
-
-</details>
-
-### Running the model on a GPU
-
-<details>
-<summary> Click to expand </summary>
-
-```python
-# pip install accelerate
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-tokenizer = AutoTokenizer.from_pretrained("tiiuae/Falcon3-7B-Base")
-model = AutoModelForCausalLM.from_pretrained("tiiuae/Falcon3-7B-Base", device_map="auto")
-
-input_text = "Question: How many hours in one day? Answer: "
-input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
-
-outputs = model.generate(input_ids)
-print(tokenizer.decode(outputs[0]))
-```
-
-</details>
-
-### Running the model on a GPU using `torch.compile`
+## Getting started
 
 <details>
 <summary> Click to expand </summary>
 
 ```python
 import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-tokenizer = AutoTokenizer.from_pretrained("tiiuae/Falcon3-7B-Base")
-model = AutoModelForCausalLM.from_pretrained("tiiuae/Falcon3-7B-Base", torch_dtype=torch.bfloat16).to(0)
-
-model = torch.compile(model)
-
-input_text = "Question: How many hours in one day? Answer: "
-input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
-
-outputs = model.generate(input_ids)
-print(tokenizer.decode(outputs[0]))
+from transformers import pipeline
+
+pipe = pipeline(
+    "text-generation", 
+    model="tiiuae/Falcon3-1B-Base", 
+    torch_dtype=torch.bfloat16, 
+    device_map="auto"
+)
+response = pipe("Question: How many hours in one day? Answer: ")
+print(response[0]['generated_text'])
 ```
 
 </details>
 
+<br>
 
-# Training Details
-
-## Training Data
-
-Falcon3-7B is trained on 15 Gigatokens of datasets comprising of web, code, STEM, high quality and mutlilingual data.
-
-## Training Procedure
-
-Falcon3-7B is trained on 256 H100 nodes (world size 2048).
+## Benchmarks
+We report in the following table our internal pipeline benchmarks:
 
-### Training Hyperparameters
 
-| **Hyperparameter** | **Value**  | **Comment**                           |
-|--------------------|------------|---------------------------------------|
-| Precision          | `bfloat16` |                                       |
-| Optimizer          | AdamW      |                                       |
-| Max learning rate  | 6e-4       | Following a WSD (warmup-stable-decay) |
-|                    |            | learning rate scheduler               |
-| Weight decay       | 1e-1       |                                       |
-| z-loss             | 1e-4       |                                       |
-| Batch size         | Variable   | Batch size was gradually increased    |
-|                    |            | during the training                   |
 
-# Evaluation
 <table border="1" style="width: 100%; text-align: center; border-collapse: collapse;">
     <colgroup>
         <col style="width: 10%;">
@@ -251,7 +186,17 @@ Falcon3-7B is trained on 256 H100 nodes (world size 2048).
     </tbody>
 </table>
 
+## Technical Report
+Coming soon....
 
+## Citation
+If Falcon3 family were helpful to your work, feel free to give us a cite.
 
-
-# Citation
+```
+@misc{Falcon3,
+    title = {The Falcon 3 family of Open Models},
+    author = {TII Team},
+    month = {December},
+    year = {2024}
+}
+```