Instructions to use letxbe/mistral-7b-v03-BoundingDocs-rephrased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use letxbe/mistral-7b-v03-BoundingDocs-rephrased with Transformers:

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

pipe = pipeline("text-generation", model="letxbe/mistral-7b-v03-BoundingDocs-rephrased")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("letxbe/mistral-7b-v03-BoundingDocs-rephrased")
model = AutoModelForCausalLM.from_pretrained("letxbe/mistral-7b-v03-BoundingDocs-rephrased", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use letxbe/mistral-7b-v03-BoundingDocs-rephrased with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "letxbe/mistral-7b-v03-BoundingDocs-rephrased"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "letxbe/mistral-7b-v03-BoundingDocs-rephrased",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/letxbe/mistral-7b-v03-BoundingDocs-rephrased

SGLang

How to use letxbe/mistral-7b-v03-BoundingDocs-rephrased 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 "letxbe/mistral-7b-v03-BoundingDocs-rephrased" \
    --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": "letxbe/mistral-7b-v03-BoundingDocs-rephrased",
		"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 "letxbe/mistral-7b-v03-BoundingDocs-rephrased" \
        --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": "letxbe/mistral-7b-v03-BoundingDocs-rephrased",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use letxbe/mistral-7b-v03-BoundingDocs-rephrased with Docker Model Runner:
```
docker model run hf.co/letxbe/mistral-7b-v03-BoundingDocs-rephrased
```

Model Card for letxbe/mistral-7b-v03-BoundingDocs-rephrased

letxbe/mistral-7b-v03-BoundingDocs-rephrased is a fine-tuned Mistral-7B-v0.3 for the Document Question Answering task. It was trained on BoundingDocs using the rephrased version of the questions.

Model Details

Model Description

Developed by: LetXBe
Model Type: LLM
Languages: Multilingual
License: CC BY 4.0
Finetuned From: Mistral-7B-v0.3
Input Format: Text using custom prompt
Output Format: JSON

🚀 How to Use

Prompt

The model should be prompted with this prompt:


TEMPLATE_PROMPT = '''<|startdocument|>
{DOCUMENT}
<|enddocument|>

<|starttask|>
Answer the following question about the document:
Question: "{QUESTION}"
Answer completing the following format:

'''json
{"value": ""}
'''

<|endtask|>
'''

where DOCUMENT is the textual content of the document page.

Inference Example

from transformers import AutoTokenizer, AutoModelForCausalLM

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("letxbe/mistral-7b-v03-BoundingDocs-rephrased")
model = AutoModelForCausalLM.from_pretrained("letxbe/mistral-7b-v03-BoundingDocs-rephrased")

# Encode input
input_text = "Your prompt"
inputs = tokenizer(input_text, return_tensors="pt")

# Generate response
outputs = model.generate(**inputs)

# Decode and print
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Safetensors

Model size

7B params

Tensor type

BF16

F16