Instructions to use ClaudioItaly/Evolutionstory-7B-v2.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ClaudioItaly/Evolutionstory-7B-v2.2 with Transformers:

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

pipe = pipeline("text-generation", model="ClaudioItaly/Evolutionstory-7B-v2.2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ClaudioItaly/Evolutionstory-7B-v2.2")
model = AutoModelForCausalLM.from_pretrained("ClaudioItaly/Evolutionstory-7B-v2.2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ClaudioItaly/Evolutionstory-7B-v2.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ClaudioItaly/Evolutionstory-7B-v2.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ClaudioItaly/Evolutionstory-7B-v2.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ClaudioItaly/Evolutionstory-7B-v2.2

SGLang

How to use ClaudioItaly/Evolutionstory-7B-v2.2 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 "ClaudioItaly/Evolutionstory-7B-v2.2" \
    --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": "ClaudioItaly/Evolutionstory-7B-v2.2",
		"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 "ClaudioItaly/Evolutionstory-7B-v2.2" \
        --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": "ClaudioItaly/Evolutionstory-7B-v2.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ClaudioItaly/Evolutionstory-7B-v2.2 with Docker Model Runner:
```
docker model run hf.co/ClaudioItaly/Evolutionstory-7B-v2.2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

merge

I finally think I managed to make a model with high writing skills. Based on the prompt it manages to be very coherent with the story.

It also has great RAG capabilities.

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

The AI model “ClaudioItaly/Evolutionstory-7B-v2.2” has achieved interesting ratings in several metrics, but also shows some areas for improvement. Here is an analysis of the main findings and implications:

Strengths: IFEval (0-Shot): Scored a very solid 48.14 in strict accuracy. This indicates that the model handles text generation tasks well without the need for prior examples, demonstrating good immediate comprehension capabilities. BBH (3-Shot): The score of 31.62 in this 3-shot dataset (where the model receives a few examples before responding) suggests that the model is able to effectively leverage additional context to improve performance. Areas of Improvement: Math and Complex Reasoning (MATH Lvl 5, 4-Shot): A score of 6.42 on this advanced math level test highlights that the model struggles with complex logic or math problems, which is typical of many general language models, which do not they are optimized for solving numerical or structured problems.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: BarraHome/Mistroll-7B-v2.2
  - model: ClaudioItaly/Evolutionstory
merge_method: slerp
base_model: ClaudioItaly/Evolutionstory
dtype: bfloat16
parameters:
  t: [0, 0.5, 1, 0.5, 0] # V shaped curve: Hermes for input & output, WizardMath in the middle layers

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Safetensors

Model size

7B params

Tensor type

BF16

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