Update model card

README.md

@@ -9,27 +9,57 @@ language:
 
 **Falcon-RW-1B is a 1B parameters causal decoder-only model built by [TII](https://www.tii.ae) and trained on 350B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb). It is made available under the [TII Falcon LLM License](https://huggingface.co/tiiuae/falcon-rw-1b/blob/main/LICENSE.txt).**
 
+*Paper coming soon 😊.*
+
 RefinedWeb is a high-quality web dataset built by leveraging stringent filtering and large-scale deduplication. Falcon-RW-1B, trained on RefinedWeb only, matches or outperforms comparable models trained on curated data.
 
-This model is intended for use as a research artifact, to study the influence of training on appropriately filtered web data alone.
+⚠️ This model is intended for use as a **research artifact**, to study the influence of training on web data alone. **If you are interested in state-of-the-art models, we recommend using Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b), both trained on >1,000 billion tokens.**
 
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import transformers
+import torch
 
-# Model Card for Falcon-RW-1B
+model = "tiiuae/falcon-rw-1b"
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+    device_map="auto",
+)
+sequences = pipeline(
+   "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
+    max_length=200,
+    do_sample=True,
+    top_k=10,
+    num_return_sequences=1,
+    eos_token_id=tokenizer.eos_token_id,
+)
+for seq in sequences:
+    print(f"Result: {seq['generated_text']}")
 
+```
+
+
+
+# Model Card for Falcon-RW-1B
 
 ## Model Details
 
 ### Model Description
 
-- **Developed by:** [https://www.tii.ae](https://www.tii.ae)
-- **Model type:** Causal decoder-only
-- **Language(s) (NLP):** English
-- **License:** TII Falcon LLM License
+- **Developed by:** [https://www.tii.ae](https://www.tii.ae);
+- **Model type:** Causal decoder-only;
+- **Language(s) (NLP):** English;
+- **License:** [TII Falcon LLM License](https://huggingface.co/tiiuae/falcon-rw-1b/blob/main/LICENSE.txt).
 
 ### Model Source
 
-- **Paper:** coming soon
-- **Demo:** coming soon
+- **Paper:** *coming soon*.
 
 ## Uses
 
@@ -39,13 +69,103 @@ Research on large language models, and the influence of adequately filtered and
 
 ### Out-of-Scope Use
 
-Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful
+Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful. 
+
+Broadly speaking, we would recommend Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) for any use not directly related to research on web data pipelines.
 
 ## Bias, Risks, and Limitations
 
-Falcon-RW models are trained on English data only, and will not generalize appropriately to other languages. Furthermore, as they are trained on a large-scale corpora representative of the web, they will carry the stereotypes and biases commonly encountered online
+Falcon-RW-1B is trained on English data only, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.
+
+### Recommendations
+
+We recommend users of Falcon-RW-1B to consider finetuning it for the specific set of tasks of interest, and for guardrails and appropriate precautions to be taken for any production use.
+
+## How to Get Started with the Model
+
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import transformers
+import torch
+
+model = "tiiuae/falcon-rw-1b"
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+    device_map="auto",
+)
+sequences = pipeline(
+   "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
+    max_length=200,
+    do_sample=True,
+    top_k=10,
+    num_return_sequences=1,
+    eos_token_id=tokenizer.eos_token_id,
+)
+for seq in sequences:
+    print(f"Result: {seq['generated_text']}")
+
+```
+
+## Training Details
+
+### Training Data
+
+Falcon-RW-1B was trained on 350B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), a high-quality filtered and deduplicated web dataset. The data was tokenized with the GPT-2 tokenizer.
+
+### Training Procedure 
+
+Falcon-RW-1B was trained on 32 A100 40GB GPUs, using only data parallelism with ZeRO.
+
+#### Training Hyperparameters
+
+Hyperparameters were adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)).
+
+- **Precision:** bf16;
+- **Optimizer:** Adam;
+- **Learning rate:** 2e-4 (500M tokens warm-up, followed by cosine decay to 2e-5);
+- **Weight decay:** 0.1;
+- **Batch size:** 512 (with a 4B tokens ramp-up).
+
+#### Speeds, Sizes, Times [optional]
+
+Training happened in early December 2022 and took about six days. 
+
+
+## Evaluation
+
+*Paper coming soon.*
+
+
+## Technical Specifications 
+
+### Model Architecture and Objective
+
+Falcon-RW-1B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).
+
+### Compute Infrastructure
+
+#### Hardware
+
+Falcon-RW-1B was trained on AWS SageMaker, on 32 A100 40GB GPUs in P4d instances. 
+
+#### Software
+
+Falcon-RW-1B was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.)
+
+
+## Citation
+
+*Paper coming soon 😊.*
+
 
+## Contact
+[email protected]
 
-## Paper
 
-More details coming soon in the paper.