Update README.md

README.md

@@ -1,21 +1,32 @@
 ---
-license: apache-2.0
+base_model:
+- tiiuae/falcon-11B
+library_name: transformers
 tags:
-- merge
 - mergekit
+- merge
 - lazymergekit
-- tiiuae/falcon-11B
+license: apache-2.0
 language:
-- nl
+- de
 ---
+# sliced
 
-# Falcon-5.5B-Dutch
+This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
 
-Falcon-5.5B-Dutch is a pruned version of Falcon-11B using [mergekit](https://github.com/cg123/mergekit):
-* [tiiuae/falcon-11B](https://huggingface.co/tiiuae/falcon-11B)
+## Merge Details
+### Merge Method
+
+This model was merged using the passthrough merge method.
+
+### Models Merged
+
+The following models were included in the merge:
 * [tiiuae/falcon-11B](https://huggingface.co/tiiuae/falcon-11B)
 
-## 🧩 Configuration
+### Configuration
+
+The following YAML configuration was used to produce this model:
 
 ```yaml
 slices:
@@ -25,13 +36,55 @@ slices:
   - sources:
       - model: tiiuae/falcon-11B
         layer_range: [56, 59]
-            
 merge_method: passthrough
 dtype: bfloat16
 ```
 
-[PruneMe](https://github.com/arcee-ai/PruneMe) has been utilized using the AgentWaller/dutch-oasst1 dataset by investigating layer similarity with 4000 samples. The layer ranges for pruning were determined based on this analysis to maintain performance while reducing model size.
+[PruneMe](https://github.com/arcee-ai/PruneMe) has been utilized using the wikimedia/wikipedia Dutch (nl) subset by investigating layer similarity with 2000 samples. The layer ranges for pruning were determined based on this analysis to maintain performance while reducing model size.
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/660c0a02cf274b3ab77dd6b7/PF3SzEhQRJPXyYi2KqS1A.png)
+![Layer Similarity Plot](https://cdn-uploads.huggingface.co/production/uploads/660c0a02cf274b3ab77dd6b7/PF3SzEhQRJPXyYi2KqS1A.png)
 
-Note: This is a base language model and has not been optimized for conversational or chat applications. Further fine-tuning may be required to adapt it for specific use cases.
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import transformers
+import torch
+
+model = "ssmits/Falcon2-5.5B-Dutch"
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    torch_dtype=torch.bfloat16,
+)
+sequences = pipeline(
+   "Can you explain the concepts of Quantum Computing?",
+    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']}")
+
+```
+
+💥 **Falcon LLMs require PyTorch 2.0 for use with `transformers`!**
+
+For fast inference with Falcon, check-out [Text Generation Inference](https://github.com/huggingface/text-generation-inference)! Read more in this [blogpost]((https://huggingface.co/blog/falcon). 
+
+## Direct Use
+Research on large language models; as a foundation for further specialization and finetuning for specific usecases (e.g., summarization, text generation, chatbot, etc.)
+
+## Out-of-Scope Use
+Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.
+
+## Bias, Risks, and Limitations
+Falcon2-5.5B is trained mostly on English, but also German, Spanish, French, Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish. It 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 Falcon2-5.5B 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.
+
+[PruneMe](https://github.com/arcee-ai/PruneMe) has been utilized using the AgentWaller/dutch-oasst1 dataset by investigating layer similarity with 4000 samples. The layer ranges for pruning were determined based on this analysis to maintain performance while reducing model size.