falcon3 release

.gitattributes

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README.md

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+---
+language:
+- en
+- fr
+- es
+- pt
+tags:
+- falcon3
+base_model: tiiuae/Falcon3-7B-Instruct
+license: other 
+license_name: falcon-llm-license 
+license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
+---
+
+<div align="center">
+    <img src="https://huggingface.co/datasets/tiiuae/documentation-images/resolve/main/general/falco3-logo.png" alt="drawing" width="500"/>
+</div>
+
+# Falcon3-7B-Instruct-GPTQ-Int4
+
+**Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B parameters.
+
+**Falcon3-7B-Instruct** achieves state-of-the-art results (at release's time) on reasoning, language understanding, instruction following, code and mathematics tasks.
+Falcon3-7B-Instruct supports 4 languages (English, French, Spanish, Portuguese) and a context length of up to 32K.
+
+This repository contains the GPTQ-quantized 4-bit instruction-tuned 7B Falcon3 model.
+
+## Model Details
+
+- Architecture
+  - Transformer-based causal decoder-only architecture
+  - 28 decoder blocks
+  - Grouped Query Attention (GQA) for faster inference: 12 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
+- Pretrained on 14 Teratokens of datasets comprising of web, code, STEM, high quality and mutlilingual data using 1024 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
+- Quantization: GPTQ 4-bit
+
+
+## Getting started
+
+<details>
+<summary> Click to expand </summary>
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+
+model_name = "tiiuae/Falcon3-7B-Instruct-GPTQ-Int4"
+
+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: 10%;">
+        <col style="width: 10%;">
+        <col style="background-color: rgba(80, 15, 213, 0.5); width: 7%;">
+    </colgroup>
+    <thead>
+        <tr>
+            <th>Benchmark</th>
+            <th>Falcon3-7B-Instruct</th>
+            <th>Falcon3-7B-Instruct-GPTQ-Int8</th>
+            <th>Falcon3-7B-Instruct-AWQ</th>
+            <th>Falcon3-7B-Instruct-GPTQ-Int4</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td>MMLU</td>
+            <td>67.68</td>
+            <td>67.55</td>
+            <td>66.33</td>
+            <td>65.55</td>
+        </tr>
+        <tr>
+            <td>MMLU-PRO</td>
+            <td>40.86</td>
+            <td>40.85</td>
+            <td>39.82</td>
+            <td>39.07</td>
+        </tr>
+        <tr>
+            <td>IFEval</td>
+            <td>75.06</td>
+            <td>76.92</td>
+            <td>74.76</td>
+            <td>72.14</td>
+        </tr>
+    </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}
+}
+```

config.json

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+{
+  "_attn_implementation_autoset": true,
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "eos_token_id": 11,
+  "head_dim": 256,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 23040,
+  "max_position_embeddings": 32768,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "pretraining_tp": 1,
+  "quantization_config": {
+    "bits": 4,
+    "checkpoint_format": "gptq",
+    "damp_percent": 0.01,
+    "desc_act": false,
+    "group_size": 128,
+    "model_file_base_name": "model",
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+    "quant_method": "gptq",
+    "static_groups": false,
+    "sym": true,
+    "true_sequential": true
+  },
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000042,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.47.0",
+  "use_cache": true,
+  "vocab_size": 131072
+}

model.safetensors

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+size 5070621392

quantize_config.json

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+  "group_size": 128,
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+  "checkpoint_format": "gptq"
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