Instructions to use rubenroy/Zurich-14B-GCv2-100k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rubenroy/Zurich-14B-GCv2-100k with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="rubenroy/Zurich-14B-GCv2-100k")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rubenroy/Zurich-14B-GCv2-100k")
model = AutoModelForCausalLM.from_pretrained("rubenroy/Zurich-14B-GCv2-100k")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rubenroy/Zurich-14B-GCv2-100k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rubenroy/Zurich-14B-GCv2-100k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rubenroy/Zurich-14B-GCv2-100k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rubenroy/Zurich-14B-GCv2-100k

SGLang

How to use rubenroy/Zurich-14B-GCv2-100k with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "rubenroy/Zurich-14B-GCv2-100k" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rubenroy/Zurich-14B-GCv2-100k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "rubenroy/Zurich-14B-GCv2-100k" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rubenroy/Zurich-14B-GCv2-100k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use rubenroy/Zurich-14B-GCv2-100k with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for rubenroy/Zurich-14B-GCv2-100k to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for rubenroy/Zurich-14B-GCv2-100k to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rubenroy/Zurich-14B-GCv2-100k to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="rubenroy/Zurich-14B-GCv2-100k",
    max_seq_length=2048,
)

Docker Model Runner
How to use rubenroy/Zurich-14B-GCv2-100k with Docker Model Runner:
```
docker model run hf.co/rubenroy/Zurich-14B-GCv2-100k
```

Zurich-14B-GCv2-100k

File size: 4,651 Bytes

---

base_model: Qwen/Qwen2.5-14B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- gammacorpus
- zurich
- chat
- conversational
license: apache-2.0
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
datasets:
- rubenroy/GammaCorpus-v2-100k
pipeline_tag: text-generation
library_name: transformers
---


![Zunich Banner](https://cdn.ruben-roy.com/AI/Zurich/img/banner-14B-100k.png)

# Zurich 14B GammaCorpus v2-100k
*A Qwen 2.5 model fine-tuned on the GammaCorpus dataset*

## Overview
Zurich 14B GammaCorpus v2-100k is a fine-tune of Alibaba's **Qwen 2.5 14B 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).

## Model Details
- **Base Model:** [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct)
- **Type:** Causal Language Models
- **Architecture:** Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
- **Number of Parameters:** 14.7B
- **Number of Paramaters (Non-Embedding):** 13.1B
- **Number of Layers:** 48
- **Number of Attention Heads (GQA):** 40 for Q and 8 for KV

## Training Details

Zurich-14B-GCv2-100k underwent fine-tuning with 1 A100 GPU for ~30 minutes and trained with the [Unsloth](https://unsloth.ai/) framework. Zurich-14B-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-14B-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 14B 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
- 50k
- **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.