Instructions to use rubenroy/Zurich-7B-GCv2-10k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rubenroy/Zurich-7B-GCv2-10k with Transformers:

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

pipe = pipeline("text-generation", model="rubenroy/Zurich-7B-GCv2-10k")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rubenroy/Zurich-7B-GCv2-10k")
model = AutoModelForCausalLM.from_pretrained("rubenroy/Zurich-7B-GCv2-10k")
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

vLLM

How to use rubenroy/Zurich-7B-GCv2-10k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rubenroy/Zurich-7B-GCv2-10k"
# 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-7B-GCv2-10k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rubenroy/Zurich-7B-GCv2-10k

SGLang

How to use rubenroy/Zurich-7B-GCv2-10k 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-7B-GCv2-10k" \
    --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-7B-GCv2-10k",
		"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-7B-GCv2-10k" \
        --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-7B-GCv2-10k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use rubenroy/Zurich-7B-GCv2-10k 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-7B-GCv2-10k 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-7B-GCv2-10k 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-7B-GCv2-10k to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="rubenroy/Zurich-7B-GCv2-10k",
    max_seq_length=2048,
)

Docker Model Runner
How to use rubenroy/Zurich-7B-GCv2-10k with Docker Model Runner:
```
docker model run hf.co/rubenroy/Zurich-7B-GCv2-10k
```

Zurich-7B-GCv2-10k / README.md

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	---
	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-v2-10k
	pipeline_tag: text-generation
	library_name: transformers
	---

	![Zunich Banner](https://cdn.ruben-roy.com/AI/Zurich/img/banner-7B-10k.png)

	# Zurich 7B GammaCorpus v2-10k
	A Qwen 2.5 model fine-tuned on the GammaCorpus dataset

	## Overview
	Zurich 7B GammaCorpus v2-10k 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 v2-10k](https://huggingface.co/datasets/rubenroy/GammaCorpus-v2-10k).

	## Model Details
	- Base Model: [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/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-GCv2-10k underwent fine-tuning with 1 T4 GPU for ~20 minutes and trained with the [Unsloth](https://unsloth.ai/) framework. Zurich-7B-GCv2-10k 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-7B-GCv2-10k"

	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:<br>
	https://huggingface.co/collections/rubenroy/gammacorpus-v1-67935e4e52a04215f15a7a60

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