add scores

README.md

@@ -1,98 +1,112 @@
 ---
-language:
-- en
-- fr
-- es
-- pt
+library_name: transformers
 tags:
 - falcon3
 base_model: tiiuae/Falcon3-1B-Base
-license: other
-license_name: falcon-llm-license
+license: other 
+license_name: falcon-llm-license 
 license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
-library_name: transformers
 ---
 
-<div align="center">
-    <img src="https://huggingface.co/datasets/tiiuae/documentation-images/resolve/main/general/falco3-logo.png" alt="drawing" width="500"/>
-</div>
 
-# Falcon3-1B-Instruct
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
 
-**Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B parameters.
 
-This repository contains the **Falcon3-1B-Instruct**. It achieves strong results on reasoning, language understanding, instruction following, code and mathematics tasks.
-Falcon3-1B-Instruct supports 4 languages (English, French, Spanish, Portuguese) and a context length of up to 8K.
 
 ## Model Details
-- Architecture
-  - Transformer-based causal decoder-only architecture
-  - 18 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
-  - 8K 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 multilingual data using 256 H100 GPU chips
-- Posttrained on 1.2 million samples of STEM, conversational, code, safety and function call data
-- 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
-
-
-## Getting started
-
-<details>
-<summary> Click to expand </summary>
-
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-
-model_name = "tiiuae/Falcon3-1B-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.
- - We use [lm-evaluation harness](https://github.com/EleutherAI/lm-evaluation-harness).
- - We report **raw scores** obtained by applying chat template and fewshot_as_multiturn.
- - We use same batch-size across all models.
+
+### 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
 
 <table border="1" style="width: 100%; text-align: center; border-collapse: collapse;">
     <colgroup>
@@ -117,90 +131,97 @@ We report in the following table our internal pipeline benchmarks.
         <tr>
             <td rowspan="3">General</td>
             <td>MMLU (5-shot)</td>
-            <td><b>68.2</b></td>
-            <td>59.8</td>
-            <td>49.2</td>
-            <td>46.1</td>
+            <td>23.4</td>
+            <td><b>58.4</b></td>
+            <td>48.4</td>
+            <td>43.9</td>
         </tr>
         <tr>
             <td>MMLU-PRO (5-shot)</td>
-            <td>16</td>
-            <td><b>28.2</b></td>
-            <td>20</td>
+            <td>11.3</td>
+            <td><b>21.3</b></td>
+            <td>17.2</td>
             <td>18.6</td>
         </tr>
         <tr>
             <td>IFEval</td>
-            <td><b>55.3</b></td>
-            <td>44.2</td>
-            <td>53</td>
+            <td><b>55.8</b></td>
+            <td>44.4</td>
+            <td>53.0</td>
             <td>54.4</td>
         </tr>
         <tr>
             <td rowspan="3">Math</td>
             <td>GSM8K (5-shot)</td>
-            <td><b>82.6</b></td>
-            <td>57.8</td>
-            <td>47.6</td>
-            <td>43.9</td>
+            <td>37.4</td>
+            <td><b>57.2</b></td>
+            <td>43.4</td>
+            <td>38.6</td>
         </tr>
         <tr>
             <td>GSM8K (8-shot, COT)</td>
-            <td>46.6</td>
-            <td><b>58.8</b></td>
-            <td>46.3</td>
-            <td>45.8</td>
+            <td>35.6</td>
+            <td><b>62.2</b></td>
+            <td>47.2</td>
+            <td>41.8</td>
         </tr>
         <tr>
             <td>MATH Lvl-5 (4-shot)</td>
-            <td><b>5.2</b></td>
-            <td>1.1</td>
-            <td>3.1</td>
-            <td>1</td>
+            <td><b>3.9</b></td>
+            <td>0.2</td>
+            <td>0.1</td>
+            <td>1.0</td>
         </tr>
         <tr>
-            <td rowspan="5">Reasoning</td>
+            <td rowspan="6">Reasoning</td>
             <td>Arc Challenge (25-shot)</td>
-            <td><b>58.6</b></td>
-            <td>50.7</td>
-            <td>49.7</td>
-            <td>47.7</td>
+            <td>34.1</td>
+            <td>47.0</td>
+            <td><b>47.6</b></td>
+            <td>45.9</td>
         </tr>
         <tr>
             <td>GPQA (0-shot)</td>
-            <td>24.4</td>
+            <td>25.3</td>
             <td><b>29.6</b></td>
-            <td>28.6</td>
+            <td>28.7</td>
             <td>26.5</td>
         </tr>
         <tr>
             <td>GPQA (0-shot, COT)</td>
             <td>13.2</td>
             <td>9.2</td>
-            <td>16</td>
+            <td>16.0</td>
             <td><b>21.3</b></td>
         </tr>
         <tr>
             <td>MUSR (0-shot)</td>
-            <td>32</td>
-            <td>36.5</td>
-            <td>32.9</td>
+            <td>32.4</td>
+            <td>36.8</td>
+            <td>33.0</td>
             <td><b>40.7</b></td>
         </tr>
         <tr>
             <td>BBH (3-shot)</td>
-            <td>33.8</td>
-            <td><b>39.2</b></td>
-            <td>34</td>
+            <td>30.3</td>
+            <td><b>38.5</b></td>
+            <td>33.1</td>
             <td>35.1</td>
         </tr>
+        <tr>
+            <td>BBH (3-shot, COT)</td>
+            <td>0.0</td>
+            <td>20.3</td>
+            <td>0.8</td>
+            <td><b>30.5</b></td>
+        </tr>
         <tr>
             <td rowspan="5">CommonSense Understanding</td>
             <td>PIQA (0-shot)</td>
             <td>72.1</td>
             <td>73.2</td>
             <td><b>74.4</b></td>
-            <td>72</td>
+            <td>72.0</td>
         </tr>
         <tr>
             <td>SciQ (0-shot)</td>
@@ -221,7 +242,7 @@ We report in the following table our internal pipeline benchmarks.
             <td>40.2</td>
             <td>40.4</td>
             <td><b>42.8</b></td>
-            <td>40</td>
+            <td>40.0</td>
         </tr>
         <tr>
             <td>MT-Bench (avg)</td>
@@ -232,7 +253,7 @@ We report in the following table our internal pipeline benchmarks.
         </tr>
         <tr>
             <td rowspan="1">Instructions following</td>
-            <td>Alpaca (WC)</td>
+            <td>Alapaca (WC)</td>
             <td><b>8.6</b></td>
             <td><b>8.6</b></td>
             <td>5.4</td>
@@ -241,22 +262,96 @@ We report in the following table our internal pipeline benchmarks.
     </tbody>
 </table>
 
-## Useful links
-- View our [release blogpost](https://huggingface.co/blog/falcon3).
-- Feel free to join [our discord server](https://discord.gg/fwXpMyGc) if you have any questions or to interact with our researchers and developers.
-
-## Technical Report
-Coming soon....
-
-## Citation
-If the Falcon3 family of models were helpful to your work, feel free to give us a cite.
- 
-```
-@misc{Falcon3,
-    title = {The Falcon 3 Family of Open Models},
-    url = {https://huggingface.co/blog/falcon3},
-    author = {Falcon-LLM Team},
-    month = {December},
-    year = {2024}
-}
-```
+### 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. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[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]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

config.json

@@ -4,6 +4,7 @@
   ],
   "attention_bias": false,
   "attention_dropout": 0.0,
+  "bos_token_id": 11,
   "eos_token_id": 11,
   "head_dim": 256,
   "hidden_act": "silu",

tokenizer_config.json

@@ -16219,7 +16219,7 @@
     ">>PASSWORD<<",
     ">>KEY<<"
   ],
-  "chat_template": "{% if tools %}{% for message in messages %}{% if message['role'] == 'system' %}{{ '<|system|>\n' + message['content'] + '\nYou are an expert in composing functions. You are given a question and a set of possible functions. \nBased on the question, you will need to make one or more function/tool calls to achieve the purpose. \nIf none of the functions can be used, point it out and refuse to answer. \nIf the given question lacks the parameters required by the function, also point it out.\n\n You have access to the following tools:\n<tools>'  + tools|tojson + '</tools>\n\nThe output MUST strictly adhere to the following format, and NO other text MUST be included.\nThe example format is as follows. Please make sure the parameter type is correct. If no function call is needed, please make the tool calls an empty list [].\n<tool_call>[\n{\"name\": \"function_name1\", \"arguments\": {\"argument1\": \"value1\", \"argument2\": \"value2\"}},\n... (more tool calls as required)\n]</tool_call>' }}{% elif message['role'] == 'user' %}{{ '<|user|>\n' + message['content'] + '\n' }}{% elif message['role'] == 'assistant' %}{% if not loop.last %}{{ '<|assistant|>\n' + message['content'] + eos_token + '\n' }}{% else %}{{ '<|assistant|>\n' + message['content'] + eos_token }}{% endif %}{% endif %}{% if loop.last and add_generation_prompt %}{{ '<|assistant|>\n' }}{% endif %}{% endfor %}{% else %}{% for message in messages %}{% if message['role'] == 'system' %}{{ '<|system|>\n' + message['content'] + '\n' }}{% elif message['role'] == 'user' %}{{ '<|user|>\n' + message['content'] + '\n' }}{% elif message['role'] == 'assistant' %}{% if not loop.last %}{{ '<|assistant|>\n' + message['content'] + eos_token + '\n' }}{% else %}{{ '<|assistant|>\n' + message['content'] + eos_token }}{% endif %}{% endif %}{% if loop.last and add_generation_prompt %}{{ '<|assistant|>\n' }}{% endif %}{% endfor %}{% endif %}",
+  "chat_template": "{% for message in messages %}{% if message['role'] == 'system' %}{{ '<|system|>\n' + message['content'] + '\n' }}{% elif message['role'] == 'user' %}{{ '<|user|>\n' + message['content'] + '\n' }}{% elif message['role'] == 'assistant' %}{% if not loop.last %}{{ '<|assistant|>\n'  + message['content'] + eos_token + '\n' }}{% else %}{{ '<|assistant|>\n'  + message['content'] + eos_token }}{% endif %}{% endif %}{% if loop.last and add_generation_prompt %}{{ '<|assistant|>\n' }}{% endif %}{% endfor %}",
   "clean_up_tokenization_spaces": true,
   "eos_token": "<|endoftext|>",
   "extra_special_tokens": {},