tiiuae
/

Falcon3-10B-Base-1.58bit

@@ -1,199 +1,80 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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-## Model Details
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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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
+## 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://github.com/microsoft/unilm/blob/master/bitnet/The-Era-of-1-bit-LLMs__Training_Tips_Code_FAQ.pdf).
+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-Base-1.58bit/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
+```
+# Evaluation
+Coming soon ..
+# Citation
+Coming soon ..