Update README.md

README.md

@@ -6,123 +6,5 @@ tags:
 base_model: tiiuae/Falcon3-3B-Base
 ---
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/62441d1d9fdefb55a0b7d12c/c-tosr0FvMlKuKQTojx_6.png)
-
-
-#  Table of Contents
-
-0. [TL;DR](#TL;DR)
-1. [Model Details](#model-details)
-2. [Training Details](#training-details)
-3. [Usage](#usage)
-4. [Evaluation](#evaluation)
-5. [Citation](#citation)
-
-
-# TL;DR
-
-# Model Details
-
-## Model Description
-
-- **Developed by:** [https://www.tii.ae](https://www.tii.ae)
-- **Model type:** Causal decoder-only
-- **Architecture:** Pure-transformer - 1.58bit version
-- **Language(s) (NLP):** Mainly English
-- **License:** TII Falcon License 2.0
-
-# Training details
-
-The model has been trained following the training strategies from the recent [1-bit LLM HF blogpost](https://huggingface.co/blog/1_58_llm_extreme_quantization) and [1-bit LLM paper](https://huggingface.co/papers/2402.17764).
-For more details about the training protocol of this model, please refer to the Falcon-3 technical report, section *Compression*.
-
-
-# Usage
-
-Currently to use this model you can either rely on Hugging Face transformers library or [BitNet](https://github.com/microsoft/BitNet) library. You can also play with the model using the [falcon-1.58bit playground](https://huggingface.co/spaces/tiiuae/falcon3-1.58bit-playground) (only for the 7B instruct version).
-
-## 🤗 transformers
-
-```python
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_id = "tiiuae/Falcon3-3B-Base-1.58bit"
-
-model = AutoModelForCausalLM.from_pretrained(
-  model_id,
-  torch_dtype=torch.bfloat16,
-).to("cuda")
-
-# Perform text generation
-```
-
-## BitNet
-
-```
-git clone https://github.com/microsoft/BitNet && cd BitNet
-pip install -r requirements.txt
-python setup_env.py --hf-repo tiiuae/Falcon3-3B-Base-1.58bit -q i2_s
-python run_inference.py -m models/Falcon3-3B-Base-1.58bit/ggml-model-i2_s.gguf -p "Hi how are you doing today?" -cnv
-```
-
-# Evaluation
-We report in the following table our internal pipeline benchmarks:
-
-**Note evaluation results are normalized score from v2 leaderboard tasks - reported results of original models in the blogpost are raw scores**
-
-<table border="1" style="width: 100%; text-align: center; border-collapse: collapse;">
-    <colgroup>
-        <col style="width: 10%;">
-        <col style="width: 10%;">
-        <col style="background-color: rgba(80, 15, 213, 0.5); width: 7%;">
-    </colgroup>
-    <thead>
-        <tr>
-            <th>Benchmark</th>
-            <th>Llama3-8B-1.58-100B-tokens</th>
-            <th>Falcon3-3B-Instruct-1.58bit </th>
-        </tr>
-    </thead>
-    <tbody>
-        <tr>
-            <td>IFEval</td>
-            <td>17.91</td>
-            <td><b>27.49</b></td>
-        </tr>      
-        <tr>
-            <td>MUSR</td>
-            <td><b>4.87</b></td>
-            <td>4.64</td>
-        </tr>
-        <tr>
-            <td>GPQA</td>
-            <td><b>1.83<b></td>
-            <td>0.00</td>
-        </tr>
-        <tr>
-            <td>BBH</td>
-            <td>5.36</td>
-            <td><b>2.97</b></td>
-        </tr>
-        <tr>
-            <td>MMLU-PRO</td>
-            <td><b>2.78<b></td>
-            <td><b>1.47</b></td>
-        </tr>      
-        <tr>
-            <td>MATH</td>
-            <td>0.26</td>
-            <td><b>0.43</b></td>
-        </tr>
-        <tr>
-            <td>Average</td>
-            <td>5.5</td>
-            <td><b>6.17</b></td>
-        </tr>          
-    </tbody>
-</table>
-
-# Citation
-
-Coming soon ..
+The Falcon3-3B-Base-1.58bit model uploaded in this warehouse is a test model downloaded from the warehouse after being tapped by Quantflip. 
+It is only used for testing Quantflip！！！