Update README.md

README.md

@@ -1,22 +1,139 @@
 ---
-base_model: unsloth/qwen2.5-1.5b-instruct-bnb-4bit
+base_model: Qwen/Qwen2.5-1.5B-Instruct
 tags:
 - text-generation-inference
 - transformers
 - unsloth
 - qwen2
 - trl
+- gammacorpus
+- zurich
+- chat
+- conversational
 license: apache-2.0
 language:
 - en
+datasets:
+- rubenroy/GammaCorpus-v2-100k
+pipeline_tag: text-generation
+library_name: transformers
 ---
 
-# Uploaded  model
+![Zunich Banner](https://cdn.ruben-roy.com/AI/Zurich/img/banner-1.5B-100k.png)
 
-- **Developed by:** rubenroy
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/qwen2.5-1.5b-instruct-bnb-4bit
+# Zurich 1.5B GammaCorpus v2-100k 
+*A Qwen 2.5 model fine-tuned on the GammaCorpus dataset*
 
-This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+## Overview
+Zurich 1.5B GammaCorpus v2-100k is a fine-tune of Alibaba's **Qwen 2.5 1.5B Instruct** model. Zurich is designed to outperform other models that have a similar size while also showcasing [GammaCorpus v2-100k](https://huggingface.co/datasets/rubenroy/GammaCorpus-v2-100k).
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+## Model Details
+- **Base Model:** [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)
+- **Type:** Causal Language Models
+- **Architecture:** Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
+- **Number of Parameters:** 1.54B
+- **Number of Paramaters (Non-Embedding)**: 1.31B
+- **Number of Layers:** 28
+- **Number of Attention Heads (GQA):** 12 for Q and 2 for KV
+
+
+## Training Details
+
+Zurich-1.5B-GCv2-100k underwent fine-tuning with 1 A100 GPU for ~10 minutes and trained with the [Unsloth](https://unsloth.ai/) framework. Zurich-1.5B-GCv2-100k was trained for **60 Epochs**. 
+
+## Usage
+
+### Requirements
+
+We **strongly** recommend you use the latest version of the `transformers` package. You may install it via `pip` as follows:
+
+```
+pip install transformers
+```
+
+### Quickstart
+
+Here is a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents;
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "rubenroy/Zurich-1.5B-GCv2-100k"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "How tall is the Eiffel tower?"
+messages = [
+    {"role": "system", "content": "You are Zurich, an AI assistant built on the Qwen 2.5 1.5B model developed by Alibaba Cloud, and fine-tuned by Ruben Roy. You are a helpful assistant."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
+
+## About GammaCorpus
+
+This model, and all Zurich models, are trained with GammaCorpus. GammaCorpus is a dataset on HuggingFace that is filled with structured and filtered multi-turn conversations.
+GammaCorpus has 4 version with different sizes in each. These are the following versions and sizes:
+
+### GammaCorpus v1
+- 10k UNFILTERED
+- 50k UNFILTERED
+- 70k UNFILTERED
+
+Here is a link to the GCv1 dataset collection:<br>
+https://huggingface.co/collections/rubenroy/gammacorpus-v1-67935e4e52a04215f15a7a60
+
+### GammaCorpus v2
+- 10k
+- 500
+- **100k  <-- This is the version of GammaCorpus v2 that the Zurich model you are using was trained on.**
+- 500k
+- 1m
+- 5m
+
+Here is a link to the GCv2 dataset collection:<br>
+https://huggingface.co/collections/rubenroy/gammacorpus-v2-67935e895e1259c404a579df
+
+### GammaCorpus CoT
+- Math 170k
+
+Here is a link to the GC-CoT dataset collection:<br>
+https://huggingface.co/collections/rubenroy/gammacorpus-cot-6795bbc950b62b1ced41d14f
+
+### GammaCorpus QA
+- Fact 450k
+
+Here is a link to the GC-QA dataset collection:<br>
+https://huggingface.co/collections/rubenroy/gammacorpus-qa-679857017bb3855234c1d8c7
+
+### The link to the full GammaCorpus dataset collection can be found [here](https://huggingface.co/collections/rubenroy/gammacorpus-67765abf607615a0eb6d61ac).
+
+## Known Limitations
+
+- **Bias:** We have tried our best to mitigate as much bias we can, but please be aware of the possibility that the model might generate some biased answers.
+
+## Additional Information
+
+### Licensing Information
+
+The model is released under the **[Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0)**. Please refer to the license for usage rights and restrictions.