Instructions to use ClaudioItaly/intelligence-cod-rag-7b-v3.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ClaudioItaly/intelligence-cod-rag-7b-v3.1 with Transformers:

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

pipe = pipeline("text-generation", model="ClaudioItaly/intelligence-cod-rag-7b-v3.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ClaudioItaly/intelligence-cod-rag-7b-v3.1")
model = AutoModelForCausalLM.from_pretrained("ClaudioItaly/intelligence-cod-rag-7b-v3.1")
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

vLLM

How to use ClaudioItaly/intelligence-cod-rag-7b-v3.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ClaudioItaly/intelligence-cod-rag-7b-v3.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ClaudioItaly/intelligence-cod-rag-7b-v3.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ClaudioItaly/intelligence-cod-rag-7b-v3.1

SGLang

How to use ClaudioItaly/intelligence-cod-rag-7b-v3.1 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/intelligence-cod-rag-7b-v3.1" \
    --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": "ClaudioItaly/intelligence-cod-rag-7b-v3.1",
		"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 "ClaudioItaly/intelligence-cod-rag-7b-v3.1" \
        --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": "ClaudioItaly/intelligence-cod-rag-7b-v3.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ClaudioItaly/intelligence-cod-rag-7b-v3.1 with Docker Model Runner:
```
docker model run hf.co/ClaudioItaly/intelligence-cod-rag-7b-v3.1
```

intelligence-cod-rag-7b-v3.1 / README.md

ClaudioItaly

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	---
	base_model:
	- AIDC-AI/Marco-o1
	- happzy2633/qwen2.5-7b-ins-v3
	library_name: transformers
	tags:
	- mergekit
	- merge

	---
	# merge

	This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

	## Merge Details
	### Merge Method

	This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [happzy2633/qwen2.5-7b-ins-v3](https://huggingface.co/happzy2633/qwen2.5-7b-ins-v3) as a base.

	### Models Merged

	The following models were included in the merge:
	* [AIDC-AI/Marco-o1](https://huggingface.co/AIDC-AI/Marco-o1)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	models:
	- model: AIDC-AI/Marco-o1
	parameters:
	density: [1, 0.8, 0.2] # Aumentato leggermente il gradiente per dare maggiore peso al contributo iniziale
	weight: 0.9 # Ridotto il peso per bilanciare meglio l'influenza
	- model: happzy2633/qwen2.5-7b-ins-v3
	parameters:
	density: 0.6 # Aumentato per consentire una maggiore fusione delle rappresentazioni
	weight: [0.1, 0.4, 0.8, 1] # Raffinato il gradiente per enfatizzare progressivamente il contributo
	- model: AIDC-AI/Marco-o1
	parameters:
	density: 0.4 # Leggermente aumentato per integrare una maggiore ricchezza di rappresentazioni
	weight:
	- filter: mlp
	value: 0.6 # Incrementato il valore per dare maggiore peso a questa componente
	- value: 0.1 # Aggiunto un piccolo peso finale per evitare contributi nulli
	merge_method: ties # Manteniamo il metodo "ties" per una fusione bilanciata
	base_model: happzy2633/qwen2.5-7b-ins-v3 # Base model per guidare la fusione
	parameters:
	normalize: true # Conserva la normalizzazione per evitare squilibri nelle rappresentazioni
	int8_mask: true # Rimane abilitato per ottimizzare le prestazioni
	adaptive_merge: true # Aggiunto per una fusione più dinamica in base al contesto
	dtype: float16 # Manteniamo float16 per limitare l'uso di memoria e migliorare l'efficienza


	```