| --- |
| datasets: |
| - tiiuae/falcon-refinedweb |
| language: |
| - en |
| inference: false |
| license: apache-2.0 |
| base_model: tiiuae/falcon-7b |
| model_creator: Technology Innovation Institute |
| model_type: causal-lm |
| pipeline_tag: text-generation |
| --- |
| |
| # 🦅 Falcon-7B Model Card (MarkAI Hosted Version) |
|
|
| <img src="https://mg-zon.vercel.app/_next/image?url=icons%2Fmarkai.png&w=48&q=75" width="300" alt="Falcon Logo"> |
|
|
| ## Model Overview |
| **Falcon-7B** is a 7 billion parameter causal decoder-only model developed by [Technology Innovation Institute (TII)](https://www.tii.ae). This repository hosts the original model weights as part of MarkAI's model collection. |
|
|
| ## Technical Specifications |
| ### Architecture |
| | Component | Specification | |
| |--------------------|----------------------------------------| |
| | Model Type | Causal Decoder-only | |
| | Attention Mechanism | Multi-Query + FlashAttention | |
| | Positional Embeddings | Rotary Positional Embeddings | |
| | Normalization | Single LayerNorm per block | |
|
|
| ### Training Details |
| | Parameter | Value | |
| |--------------------|----------------------------------------| |
| | Training Tokens | 1,500B (1.5 trillion) | |
| | Training Compute | 384 × A100 40GB GPUs (P4d instances) | |
| | Training Time | ~2 weeks | |
| | Precision | bfloat16 | |
|
|
| ## Usage Examples |
|
|
| ### Text Generation |
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| import torch |
| |
| model = AutoModelForCausalLM.from_pretrained( |
| "ibrahimlasfar/MarkAI", |
| device_map="auto", |
| torch_dtype=torch.bfloat16, |
| trust_remote_code=True |
| ) |
| tokenizer = AutoTokenizer.from_pretrained("ibrahimlasfar/MarkAI") |
| |
| inputs = tokenizer( |
| "The future of artificial intelligence", |
| return_tensors="pt" |
| ).to("cuda") |
| outputs = model.generate( |
| **inputs, |
| max_length=100, |
| do_sample=True, |
| top_k=10 |
| ) |
| print(tokenizer.decode(outputs[0])) |
