Instructions to use AliCat2/Picaro-24b-2506-424 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AliCat2/Picaro-24b-2506-424 with Transformers:

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

pipe = pipeline("text-generation", model="AliCat2/Picaro-24b-2506-424")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AliCat2/Picaro-24b-2506-424")
model = AutoModelForCausalLM.from_pretrained("AliCat2/Picaro-24b-2506-424", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AliCat2/Picaro-24b-2506-424 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AliCat2/Picaro-24b-2506-424"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AliCat2/Picaro-24b-2506-424",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AliCat2/Picaro-24b-2506-424

SGLang

How to use AliCat2/Picaro-24b-2506-424 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 "AliCat2/Picaro-24b-2506-424" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AliCat2/Picaro-24b-2506-424",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "AliCat2/Picaro-24b-2506-424" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AliCat2/Picaro-24b-2506-424",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AliCat2/Picaro-24b-2506-424 with Docker Model Runner:
```
docker model run hf.co/AliCat2/Picaro-24b-2506-424
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Instruct Styles (Text Completion)

No instruct <- Recommended (Seems to work well!)
Modified ChatML (input/output)
Mistral v7

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the Passthrough merge method.

Models Merged

The following models were included in the merge:

anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML + Trappu/Picaro-24b-2506-adapters-424steps

Configuration

The following YAML configuration was used to produce this model:

merge_method: passthrough
dtype: bfloat16
models:
  - model: anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML+Trappu/Picaro-24b-2506-adapters-424steps

Downloads last month: 4

Safetensors

Model size

24B params

Tensor type

BF16

Model tree for AliCat2/Picaro-24b-2506-424

Base model

mistralai/Mistral-Small-3.1-24B-Base-2503

Finetuned

mistralai/Mistral-Small-3.2-24B-Instruct-2506

Finetuned

anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML

Finetuned

(3)

this model

Quantizations

1 model