Instructions to use mayflowergmbh/occiglot-7b-es-en-instruct-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mayflowergmbh/occiglot-7b-es-en-instruct-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="mayflowergmbh/occiglot-7b-es-en-instruct-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mayflowergmbh/occiglot-7b-es-en-instruct-AWQ")
model = AutoModelForCausalLM.from_pretrained("mayflowergmbh/occiglot-7b-es-en-instruct-AWQ", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mayflowergmbh/occiglot-7b-es-en-instruct-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mayflowergmbh/occiglot-7b-es-en-instruct-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mayflowergmbh/occiglot-7b-es-en-instruct-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mayflowergmbh/occiglot-7b-es-en-instruct-AWQ

SGLang

How to use mayflowergmbh/occiglot-7b-es-en-instruct-AWQ 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 "mayflowergmbh/occiglot-7b-es-en-instruct-AWQ" \
    --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": "mayflowergmbh/occiglot-7b-es-en-instruct-AWQ",
		"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 "mayflowergmbh/occiglot-7b-es-en-instruct-AWQ" \
        --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": "mayflowergmbh/occiglot-7b-es-en-instruct-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mayflowergmbh/occiglot-7b-es-en-instruct-AWQ with Docker Model Runner:
```
docker model run hf.co/mayflowergmbh/occiglot-7b-es-en-instruct-AWQ
```

johannhartmann commited on Apr 1, 2024

Commit

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+---
+license: apache-2.0
+language:
+- en
+- es
+pipeline_tag: text-generation
+---
+![image/png](https://huggingface.co/datasets/malteos/images/resolve/main/occiglot.medium.png)
+# Occiglot-7B-ES-EN
+> A [polyglot](https://en.wikipedia.org/wiki/Multilingualism#In_individuals) language model for the [Occident](https://en.wikipedia.org/wiki/Occident).
+>
+**Occiglot-7B-ES-EN** is a generative language model with 7B parameters for Spanish and English and trained by the [Occiglot Research Collective](https://occiglot.github.io/occiglot/).
+It is based on [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) and trained on 112B tokens of additional multilingual and code data with a block size of 8,192 tokens per sample.
+Note that the model is a general-purpose base model and was not instruction-fine-tuned nor optimized for chat or other applications. We make an instruction tuned variant available as [occiglot-7b-es-en-instruct](https://huggingface.co/occiglot/occiglot-7b-es-en-instruct)
+This is the first release of an ongoing open research project for multilingual language models.
+If you want to train a model for your own language or are working on evaluations, please contact us or join our [Discord server](https://discord.gg/wUpvYs4XvM). **We are open for collaborations!**
+### Model details
+- **Continued-pretraining from:** [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
+- **Model type:** Causal decoder-only transformer language model
+- **Languages:** English, Spanish, and code.
+- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0.html)
+- **Compute resources:** [HessianAI's 42](https://hessian.ai/)
+- **Contributors:** Manuel Brack, Patrick Schramowski, Pedro Ortiz, Malte Ostendorff, Fabio Barth, Georg Rehm, Kristian Kersting
+- **Research labs:** [Occiglot](https://occiglot.github.io/occiglot/) with support from [SAINT](https://www.dfki.de/en/web/research/research-departments/foundations-of-systems-ai) and [SLT](https://www.dfki.de/en/web/research/research-departments/speech-and-language-technology)
+- **Contact:** [Discord](https://discord.gg/wUpvYs4XvM)
+### How to use
+You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
+set a seed for reproducibility:
+```python
+>>> from transformers import pipeline, set_seed
+>>> generator = pipeline('text-generation', model='occiglot/occiglot-7b-es-en')
+>>> set_seed(42)
+>>> generator("Hola, soy una modelo lingüística", max_length=40, num_return_sequences=1)
+[{'generated_text': 'Hola, soy una modelo lingüística que puede ayudarte a traducir textos entre español e inglés. Si me envías un texto en español'}]
+```
+## Dataset
+The training data is the respective subset of the data used for [occiglot-7b-eu5](https://huggingface.co/occiglot/occiglot-7b-eu5), i.e. Spanish plus English and Code.
+The data distribution by language (estimated) is as follows:
+- English: ~34%
+- Code: ~13%
+- Spanish: ~52%
+The training data was prepared using [lm-datasets](https://github.com/malteos/lm-datasets).
+The exact data configuration is [here](https://huggingface.co/occiglot/occiglot-7b-eu5/blob/main/lm-datasets-config.yml).
+## Training settings
+- Continual pre-training on 128 x A100-80GB on [HessianAI's 42](https://hessian.ai/).
+- Framework: [Determined](https://www.determined.ai/)
+- Precision: bf16
+- Optimizer: AdamW (lr: 0.00001, warmup_steps: 420)
+- Global batch size: 512 (with 8192 blocksize) split over 128 GPUs
+- Cosine Annealing with Warmup
+## Tokenizer
+Tokenizer is unchanged from [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1).
+## Evaluation
+Preliminary evaluation results can be found below.
+Please note that the non-English results are based on partially machine-translated datasets and English prompts ([Belebele](https://huggingface.co/datasets/facebook/belebele) and [Okapi framework](https://github.com/nlp-uoregon/Okapi)) and thus should be interpreted with caution, e.g., biased towards English model performance.
+Currently, we are working on more suitable benchmarks for Spanish, French, German, and Italian.
+<details>
+<summary>Evaluation results</summary>
+### All 5 Languages
+|                            |      avg |   arc_challenge |   belebele |   hellaswag |     mmlu |   truthfulqa |
+|:---------------------------|---------:|----------------:|-----------:|------------:|---------:|-------------:|
+| Occiglot-7b-eu5            | 0.516895 |        0.508109 |   0.675556 |    0.718963 | 0.402064 |     0.279782 |
+| Occiglot-7b-eu5-instruct   | 0.537799 |        0.53632  |   0.691111 |    0.731918 | 0.405198 |     0.32445  |
+| Occiglot-7b-es-en          | 0.483388 |        0.482949 |   0.606889 |    0.653902 | 0.398922 |     0.274277 |
+| Occiglot-7b-es-en-instruct | 0.504023 |        0.494576 |   0.65     |    0.670847 | 0.406176 |     0.298513 |
+| Lince-mistral-7b-it-es     | 0.543427 |        0.540222 |   0.745111 |    0.692931 | 0.426241 |     0.312629 |
+| Mistral-7b-v0.1            | 0.547111 |        0.528937 |   0.768444 |    0.682516 | 0.448253 |     0.307403 |
+| Mistral-7b-instruct-v0.2   | 0.56713  |        0.547228 |   0.741111 |    0.69455  | 0.422501 |     0.430262 |
+### English
+|                            |      avg |   arc_challenge |   belebele |   hellaswag |     mmlu |   truthfulqa |
+|:---------------------------|---------:|----------------:|-----------:|------------:|---------:|-------------:|
+| Occiglot-7b-eu5            | 0.59657  |        0.530717 |   0.726667 |    0.789882 | 0.531904 |     0.403678 |
+| Occiglot-7b-eu5-instruct   | 0.617905 |        0.558874 |   0.746667 |    0.799841 | 0.535109 |     0.449    |
+| Occiglot-7b-es-en          | 0.593609 |        0.543515 |   0.697778 |    0.788289 | 0.548355 |     0.390109 |
+| Occiglot-7b-es-en-instruct | 0.615707 |        0.552048 |   0.736667 |    0.797451 | 0.557328 |     0.435042 |
+| Leo-mistral-hessianai-7b   | 0.600949 |        0.522184 |   0.736667 |    0.777833 | 0.538812 |     0.429248 |
+| Mistral-7b-v0.1            | 0.668385 |        0.612628 |   0.844444 |    0.834097 | 0.624555 |     0.426201 |
+| Mistral-7b-instruct-v0.2   | 0.713657 |        0.637372 |   0.824444 |    0.846345 | 0.59201  |     0.668116 |
+### Spanish
+|                            |      avg |   arc_challenge_es |   belebele_es |   hellaswag_es |   mmlu_es |   truthfulqa_es |
+|:---------------------------|---------:|-------------------:|--------------:|---------------:|----------:|----------------:|
+| Occiglot-7b-eu5            | 0.533194 |           0.508547 |      0.676667 |       0.725411 |  0.499325 |        0.25602  |
+| Occiglot-7b-eu5-instruct   | 0.548155 |           0.535043 |      0.68     |       0.737039 |  0.503525 |        0.285171 |
+| Occiglot-7b-es-en          | 0.527264 |           0.529915 |      0.627778 |       0.72253  |  0.512749 |        0.243346 |
+| Occiglot-7b-es-en-instruct | 0.5396   |           0.545299 |      0.636667 |       0.734372 |  0.524374 |        0.257288 |
+| Lince-mistral-7b-it-es     | 0.547212 |           0.52906  |      0.721111 |       0.687967 |  0.512749 |        0.285171 |
+| Mistral-7b-v0.1            | 0.554817 |           0.528205 |      0.747778 |       0.672712 |  0.544023 |        0.281369 |
+| Mistral-7b-instruct-v0.2   | 0.568575 |           0.54188  |      0.73     |       0.685406 |  0.511699 |        0.373891 |
+</details>
+## Acknowledgements
+The model training was supported by a compute grant at the [42 supercomputer](https://hessian.ai/)  which is a central component in the development of [hessian AI](https://hessian.ai/), the [AI Innovation Lab](https://hessian.ai/infrastructure/ai-innovationlab/) (funded by the [Hessian Ministry of Higher Education, Research and the Art (HMWK)](https://wissenschaft.hessen.de) & the [Hessian Ministry of the Interior, for Security and Homeland Security (HMinD)](https://innen.hessen.de)) and the [AI Service Centers](https://hessian.ai/infrastructure/ai-service-centre/) (funded by the [German Federal Ministry for Economic Affairs and Climate Action (BMWK)](https://www.bmwk.de/Navigation/EN/Home/home.html)).
+The curation of the training data is partially funded by the [German Federal Ministry for Economic Affairs and Climate Action (BMWK)](https://www.bmwk.de/Navigation/EN/Home/home.html)
+through the project [OpenGPT-X](https://opengpt-x.de/en/) (project no. 68GX21007D).
+## License
+[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0.html)
+## See also
+- https://huggingface.co/collections/occiglot/occiglot-eu5-7b-v01-65dbed502a6348b052695e01