Instructions to use rubenroy/Zurich-1.5B-GCv2-100k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rubenroy/Zurich-1.5B-GCv2-100k with Transformers:

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

pipe = pipeline("text-generation", model="rubenroy/Zurich-1.5B-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-1.5B-GCv2-100k")
model = AutoModelForCausalLM.from_pretrained("rubenroy/Zurich-1.5B-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-1.5B-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-1.5B-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-1.5B-GCv2-100k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rubenroy/Zurich-1.5B-GCv2-100k

SGLang

How to use rubenroy/Zurich-1.5B-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-1.5B-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-1.5B-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-1.5B-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-1.5B-GCv2-100k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use rubenroy/Zurich-1.5B-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-1.5B-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-1.5B-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-1.5B-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-1.5B-GCv2-100k",
    max_seq_length=2048,
)

Docker Model Runner
How to use rubenroy/Zurich-1.5B-GCv2-100k with Docker Model Runner:
```
docker model run hf.co/rubenroy/Zurich-1.5B-GCv2-100k
```

lbourdois commited on Apr 28, 2025

Commit

8975882

verified ·

1 Parent(s): 2b2c834

Improve language tag

Browse files

Hi! As the model is multilingual, this is a PR to add other languages than English to the language tag to improve the referencing. Note that 29 languages are announced in the README, but only 13 are explicitly listed. I was therefore only able to add these 13 languages.

Files changed (1) hide show

README.md +150 -138

README.md CHANGED Viewed

@@ -1,139 +1,151 @@
----
-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
----
-![Zunich Banner](https://cdn.ruben-roy.com/AI/Zurich/img/banner-1.5B-100k.png)
-# Zurich 1.5B GammaCorpus v2-100k
-*A Qwen 2.5 model fine-tuned on the GammaCorpus dataset*
-## 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).
-## 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.

+---
+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:
+- 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-1.5B-100k.png)
+# Zurich 1.5B GammaCorpus v2-100k
+*A Qwen 2.5 model fine-tuned on the GammaCorpus dataset*
+## 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).
+## 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.