Instructions to use DeepMount00/Mistral-RAG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DeepMount00/Mistral-RAG with Transformers:

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

pipe = pipeline("text-generation", model="DeepMount00/Mistral-RAG")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DeepMount00/Mistral-RAG")
model = AutoModelForCausalLM.from_pretrained("DeepMount00/Mistral-RAG")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DeepMount00/Mistral-RAG with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DeepMount00/Mistral-RAG"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DeepMount00/Mistral-RAG",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DeepMount00/Mistral-RAG

SGLang

How to use DeepMount00/Mistral-RAG 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 "DeepMount00/Mistral-RAG" \
    --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": "DeepMount00/Mistral-RAG",
		"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 "DeepMount00/Mistral-RAG" \
        --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": "DeepMount00/Mistral-RAG",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DeepMount00/Mistral-RAG with Docker Model Runner:
```
docker model run hf.co/DeepMount00/Mistral-RAG
```

DeepMount00 commited on Apr 21, 2024

Commit

de0afc4

verified ·

1 Parent(s): 0150041

Update README.md

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README.md +6 -8

README.md CHANGED Viewed

@@ -7,26 +7,24 @@ language:
 - it
 ---
-# Mistral-RAG
-## Model Details
 - **Model Name:** Mistral-RAG
 - **Base Model:** Mistral-Ita-7b
 - **Specialization:** Question and Answer Tasks
-## Overview
 Mistral-RAG is a refined fine-tuning of the Mistral-Ita-7b model, engineered specifically to enhance question and answer tasks. It features a unique dual-response capability, offering both generative and extractive modes to cater to a wide range of informational needs.
-## Capabilities
-### Generative Mode
 - **Description:** The generative mode is designed for scenarios that require complex, synthesized responses. This mode integrates information from multiple sources and provides expanded explanations.
 - **Ideal Use Cases:**
   - Educational purposes
   - Advisory services
   - Creative scenarios where depth and detailed understanding are crucial
-### Extractive Mode
 - **Description:** The extractive mode focuses on speed and precision. It delivers direct and concise answers by extracting specific data from texts.
 - **Ideal Use Cases:**
   - Factual queries in research
@@ -34,7 +32,7 @@ Mistral-RAG is a refined fine-tuning of the Mistral-Ita-7b model, engineered spe
   - Professional environments where accuracy and direct evidence are necessary
-## How to Use
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer

 - it
 ---
+## Mistral-RAG
 - **Model Name:** Mistral-RAG
 - **Base Model:** Mistral-Ita-7b
 - **Specialization:** Question and Answer Tasks
+### Overview
 Mistral-RAG is a refined fine-tuning of the Mistral-Ita-7b model, engineered specifically to enhance question and answer tasks. It features a unique dual-response capability, offering both generative and extractive modes to cater to a wide range of informational needs.
+### Capabilities
+#### Generative Mode
 - **Description:** The generative mode is designed for scenarios that require complex, synthesized responses. This mode integrates information from multiple sources and provides expanded explanations.
 - **Ideal Use Cases:**
   - Educational purposes
   - Advisory services
   - Creative scenarios where depth and detailed understanding are crucial
+#### Extractive Mode
 - **Description:** The extractive mode focuses on speed and precision. It delivers direct and concise answers by extracting specific data from texts.
 - **Ideal Use Cases:**
   - Factual queries in research
   - Professional environments where accuracy and direct evidence are necessary
+### How to Use
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer