Instructions to use catallama/CataLlama-v0.2-Instruct-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use catallama/CataLlama-v0.2-Instruct-SFT with Transformers:

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

pipe = pipeline("text-generation", model="catallama/CataLlama-v0.2-Instruct-SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("catallama/CataLlama-v0.2-Instruct-SFT")
model = AutoModelForCausalLM.from_pretrained("catallama/CataLlama-v0.2-Instruct-SFT", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use catallama/CataLlama-v0.2-Instruct-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "catallama/CataLlama-v0.2-Instruct-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "catallama/CataLlama-v0.2-Instruct-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/catallama/CataLlama-v0.2-Instruct-SFT

SGLang

How to use catallama/CataLlama-v0.2-Instruct-SFT 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 "catallama/CataLlama-v0.2-Instruct-SFT" \
    --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": "catallama/CataLlama-v0.2-Instruct-SFT",
		"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 "catallama/CataLlama-v0.2-Instruct-SFT" \
        --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": "catallama/CataLlama-v0.2-Instruct-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use catallama/CataLlama-v0.2-Instruct-SFT with Docker Model Runner:
```
docker model run hf.co/catallama/CataLlama-v0.2-Instruct-SFT
```

CataLlama-v0.2-Instruct-SFT / README.md

laurentiubp

Update README.md

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	---
	license: llama3
	base_model: catallama/CataLlama-v0.2-Base
	tags:
	- llama
	- llama-3
	- Catalan
	model-index:
	- name: CataLlama-v0.2-Instruct-SFT
	results: []
	datasets:
	- catallama/Catalan-Instruct-V2
	language:
	- ca
	- en
	pipeline_tag: text-generation
	library_name: transformers
	---

	![](https://huggingface.co/catallama/CataLlama-v0.2-Instruct-SFT/resolve/main/CataLlama-v0.2.png)

	# CataLlama-v0.2-Instruct-SFT

	CataLlama-v0.2-Instruct-SFT is an instruct fine-tune of [catallama/CataLlama-v0.2-Base](https://huggingface.co/catallama/CataLlama-v0.2-Base) on the [catallama/Catalan-Instruct-V2](https://huggingface.co/datasets/catallama/Catalan-Instruct-V2) dataset.

	CataLlama-v0.2 was trained on roughly 620 million new tokens which is almost 40% more than CataLlama-v0.1.

	This new (V2) SFT Dataset was built mostly from scratch and it only retained parts of the V1.

	On top of the existing instructions in Catalan, 250k additional instructions were translated for this model.

	All the English instructions existing in the V1 of the dataset were discarded and replaced with high quality instructions scored with [RLHFlow/ArmoRM-Llama3-8B-v0.1](https://huggingface.co/RLHFlow/ArmoRM-Llama3-8B-v0.1) reward model.

	The model shows improved proficiency with the Catalan language while performing significantly better than CataLlama-v0.1 on all tasks.

	This is an instruction fine-tuned model proficient on the following tasks in Catalan

	- Information extraction (suitable for RAG)
	- Named Entity Recognition (NER)
	- Translation from English to Catalan and Catalan to English
	- Summarization - both short form and long form
	- Sentiment analysis
	- Chat

	Model developers [Laurentiu Petrea](https://www.linkedin.com/in/laurentiupetrea/) based on Llama-3 from Meta.

	Model Architecture CataLlama is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and direct preference optimisation (DPO) to align with human preferences for helpfulness and safety.

	License The model uses the llama-3 license available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)

	## Benchmarks

	\| Model \| CataLlama-v0.1-Instruct-SFT \| CataLlama-v0.2-Instruct-SFT \|
	\| ------------------ \| --------------------------- \| ------------------------------- \|
	\| MMLU 5 shot \| 55.28 \| 59.35 \|
	\| GSM8K cot 8 shot \| 51.63 \| 76.04 \|

	### Use with transformers

	See the snippet below for usage with Transformers:

	The model follows the same prompt template as Llama-3 Instruct

	```python
	import transformers
	import torch

	model_id = "catallama/CataLlama-v0.2-Instruct-SFT"

	pipeline = transformers.pipeline(
	"text-generation",
	model=model_id,
	model_kwargs={"torch_dtype": torch.bfloat16},
	device_map="auto",
	)

	messages = [
	{"role": "user", "content": "Ei com estàs avui?"},
	]

	prompt = pipeline.tokenizer.apply_chat_template(
	messages,
	tokenize=False,
	add_generation_prompt=True
	)

	outputs = pipeline(
	prompt,
	max_new_tokens=1024,
	do_sample=True,
	temperature=0.6,
	top_p=0.9,
	)

	print(outputs[0]["generated_text"][len(prompt):])
	```

	## Training procedure

	The model was trained with the same prompt template of Llama-3 Instruct.

	The model was trained for two epochs on 8x A100 80GB GPUs using DeepSpeed ZeRO State-3 without CPU offloading.


	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 2e-05
	- distributed_type: multi-GPU
	- num_devices: 8
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: cosine
	- lr_scheduler_warmup_steps: 100
	- num_epochs: 2


	## Intended Use

	Note: This model is not intended to beat benchmarks, but to demonstrate techniques for augmenting LLMs on new languages and preserve rare languages as part of our world heritage.

	Intended Use Cases Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.

	Out-of-scope Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.

	**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.