metadata

base_model: Qwen/Qwen2.5-7B-Instruct
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - qwen2
  - trl
  - gammacorpus
  - zurich
  - chat
  - conversational
license: apache-2.0
language:
  - en
datasets:
  - rubenroy/GammaCorpus-v2-500k
pipeline_tag: text-generation
library_name: transformers

Zurich 7B GammaCorpus v2-500k

A Qwen 2.5 model fine-tuned on the GammaCorpus dataset

Overview

Zurich 7B GammaCorpus v2-500k is a fine-tune of Alibaba's Qwen 2.5 7B Instruct model. Zurich is designed to outperform other models that have a similar size while also showcasing GammaCorpus v2-500k.

Model Details

Base Model: Qwen/Qwen2.5-7B-Instruct
Type: Causal Language Models
Architecture: Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
Number of Parameters: 7.61B
Number of Paramaters (Non-Embedding): 6.53B
Number of Layers: 28
Number of Attention Heads (GQA): 28 for Q and 4 for KV

Training Details

Zurich-7B-GCv2-500k underwent fine-tuning with 1 T4 GPU for ~290 minutes and trained with the Unsloth framework. Zurich-7B-GCv2-500k 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;

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "rubenroy/Zurich-7b-GCv2-500k"

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 7b 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:
https://huggingface.co/collections/rubenroy/gammacorpus-v1-67935e4e52a04215f15a7a60

GammaCorpus v2

10k
50k
100k
500k <-- This is the version of GammaCorpus v2 that the Zurich model you are using was trained on.
1m
5m

Here is a link to the GCv2 dataset collection:
https://huggingface.co/collections/rubenroy/gammacorpus-v2-67935e895e1259c404a579df

GammaCorpus CoT

Math 170k

Here is a link to the GC-CoT dataset collection:
https://huggingface.co/collections/rubenroy/gammacorpus-cot-6795bbc950b62b1ced41d14f

GammaCorpus QA

Fact 450k

Here is a link to the GC-QA dataset collection:
https://huggingface.co/collections/rubenroy/gammacorpus-qa-679857017bb3855234c1d8c7

The link to the full GammaCorpus dataset collection can be found here.

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.

Licence:

The model is released under the Apache 2.0 License. Please refer to the license for usage rights and restrictions.