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Model: tiiuae/Falcon3-10B-Base-1.58bit-prequantized
Source: Original Platform

README.md

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+
+---
+library_name: transformers
+tags:
+- bitnet
+- falcon3
+base_model: tiiuae/Falcon3-10B-Base
+license: other 
+license_name: falcon-llm-license 
+license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
+---
+
+
+![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
+
+This is a 'hacked' version of `tiiuae/Falcon3-10B-Base-1.58bit` where model weight scales have been injected into ternary model weights in order to make the model compatible with fine-tuning
+
+## 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/Falcon3-10B-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/Falcon3-10B-Base-1.58bit -q i2_s
+python run_inference.py -m models/Falcon3-10B-1.58bit/ggml-model-i2_s.gguf -p "You are a helpful assistant" -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>Falcon3-10B-Base-1.58bit</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td>IFEval</td>
+            <td>17.91</td>
+            <td><b>24.89</b></td>
+        </tr>      
+        <tr>
+            <td>MUSR</td>
+            <td><b>4.87</b></td>
+            <td>4.6</td>
+        </tr>
+        <tr>
+            <td>GPQA</td>
+            <td>1.83</td>
+            <td>1.83</td>
+        </tr>
+        <tr>
+            <td>BBH</td>
+            <td><b>5.36</b></td>
+            <td>4.44</td>
+        </tr>
+        <tr>
+            <td>MMLU-PRO</td>
+            <td><b>2.78</b></td>
+            <td>1.36</td>
+        </tr>      
+        <tr>
+            <td>MATH</td>
+            <td>0.26</td>
+            <td><b>0.48</b></td>
+        </tr>
+        <tr>
+            <td>Average</td>
+            <td>5.5</td>
+            <td><b>6.27</b></td>
+        </tr>          
+    </tbody>
+</table>
+
+# Citation
+
+Coming soon ..