Instructions to use Activ-Hop/hop-0.41-RAW-RAG-mistral-16bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Activ-Hop/hop-0.41-RAW-RAG-mistral-16bit with Transformers:

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

pipe = pipeline("text-generation", model="Activ-Hop/hop-0.41-RAW-RAG-mistral-16bit")

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

tokenizer = AutoTokenizer.from_pretrained("Activ-Hop/hop-0.41-RAW-RAG-mistral-16bit")
model = AutoModelForCausalLM.from_pretrained("Activ-Hop/hop-0.41-RAW-RAG-mistral-16bit", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Activ-Hop/hop-0.41-RAW-RAG-mistral-16bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Activ-Hop/hop-0.41-RAW-RAG-mistral-16bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Activ-Hop/hop-0.41-RAW-RAG-mistral-16bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Activ-Hop/hop-0.41-RAW-RAG-mistral-16bit

SGLang

How to use Activ-Hop/hop-0.41-RAW-RAG-mistral-16bit 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 "Activ-Hop/hop-0.41-RAW-RAG-mistral-16bit" \
    --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": "Activ-Hop/hop-0.41-RAW-RAG-mistral-16bit",
		"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 "Activ-Hop/hop-0.41-RAW-RAG-mistral-16bit" \
        --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": "Activ-Hop/hop-0.41-RAW-RAG-mistral-16bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use Activ-Hop/hop-0.41-RAW-RAG-mistral-16bit with Docker Model Runner:
```
docker model run hf.co/Activ-Hop/hop-0.41-RAW-RAG-mistral-16bit
```

Hop 0.41 RAW RAG Mistral

Developed by: Activ-Hop
License: apache-2.0
Finetuned from model : unsloth/mistral-7b-bnb-4bit
Dataset used : Activ-Hop/hop-0.4-rag [1500 samples]

This is a mistral based model fine-tuned with Unsloth and Huggingface's TRL library.

Merged 16bit.

max_seq_length = 4096

LoRA r: 64 -> 2.26% of all Params
LoRA alpha: 128
Training: 1 epoch (187 steps)

RAG Skills pretty damn good. Next one will be probably a A+RAG model.

The prompt template is as follows:

"""<|systeme|>Tu es Hop, un chatbot représentant l'école d'ingénieurs ESAIP. Ton rôle est d'aider et d'assister des étudiants et des adultes sur des sujets concernant l'école, les formations, mais aussi de sensibiliser aux enjeux du numérique et de la gestion des risques pour un avenir responsable.
<|documents|>{}
<|question|>{}
<|reponse|>{}"""

GGUF models -> Activ-Hop/hop-0.41-RAW-RAG-mistral-gguf
LoRA adapters -> Activ-Hop/hop-0.41-RAW-RAG-mistral-lora
Dataset -> Activ-Hop/hop-0.4-rag

PS: I finally remembered that alpha/r ratio for LoRA should always be higher than 1... next one should have a higher alpha.

Downloads last month: 18

Model tree for Activ-Hop/hop-0.41-RAW-RAG-mistral-16bit

Base model

unsloth/mistral-7b-bnb-4bit

Finetuned

(631)

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