Update README.md

README.md

@@ -2,39 +2,36 @@
 base_model: google/gemma-2-2B
 license: cc-by-nc-sa-4.0
 language:
-  - de
-  - nl
-  - is
-  - es
-  - fr
-  - pt
-  - uk
-  - hi
-  - zh
-  - ru
-  - cs
-  - ko
-  - ja
-  - it
-  - en
-  - da
-  - pl
-  - hu
-  - sv
-  - 'no'
-  - ro
-  - fi
+- de
+- nl
+- is
+- es
+- fr
+- pt
+- uk
+- hi
+- zh
+- ru
+- cs
+- ko
+- ja
+- it
+- en
+- da
+- pl
+- hu
+- sv
+- 'no'
+- ro
+- fi
 library_name: transformers
-datasets:
-  - Widn/TowerBlocks-v4-250205
-  - Widn/TowerDPO-v4-anthill-250227
 ---
 
 # Model Description:
 
-**Tower-v4-Anthill** is build on top of Gemma 2 2B. The model goes through the Continuous Pretraining (CPT), Instruction Tuning (IT), Weighted Preference Optimization (WPO) and GRPO with verifiable rewards. During all stages we include parallel and multilingual data (covering 22 languages).
+**Tower+ 2B** is build on top of Gemma 2 2B. The model goes through the Continuous Pretraining (CPT), Instruction Tuning (IT), Weighted Preference Optimization (WPO) and GRPO with verifiable rewards. During all stages we include parallel and multilingual data (covering 22 languages).
 
-This approach makes Tower v4 Anthill one of the best multilingual LLMs under 3B parameters.
+This approach makes Tower+ 2B one of the best multilingual LLMs under 3B parameters.
 
 - **Developed by:** Widn
 - **Model type:** A 2B parameter model fine-tuned on a mix of _translation-related tasks_ as well as  _general instruction-following_ datasets that include reasoning, code instructions, etc.
@@ -80,7 +77,7 @@ sampling_params = SamplingParams(
   temperature=0,
   max_tokens=8192,
 )
-llm = LLM(model="Widn/Tower-4-Anthill", tensor_parallel_size=1)
+llm = LLM(model="Unbabel/Tower-Plus-2B", tensor_parallel_size=1)
 messages = [{"role": "user", "content": "Translate: Hello, world! into Portuguese."}]
 outputs = llm.chat(messages, sampling_params)
 # Make sure your prompt_token_ids look like this
@@ -97,10 +94,10 @@ print (outputs[0].outputs[0].text)
 import torch
 from transformers import pipeline
 
-pipe = pipeline("text-generation", model="Widn/Tower-4-Anthill", device_map="auto")
+pipe = pipeline("text-generation", model="Unbabel/Tower-Plus-2B", device_map="auto")
 # We use the tokenizer’s chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
 messages = [{"role": "user", "content": "Translate: Hello, world! into Portuguese."}]
 input_ids = pipe.tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True)
 outputs = pipe(messages, max_new_tokens=256, do_sample=False)
 print(outputs[0]["generated_text"])
-```
+```