Instructions to use Arc53/docsgpt-7b-mistral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Arc53/docsgpt-7b-mistral with Transformers:

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

pipe = pipeline("text-generation", model="Arc53/docsgpt-7b-mistral")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Arc53/docsgpt-7b-mistral")
model = AutoModelForCausalLM.from_pretrained("Arc53/docsgpt-7b-mistral")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Arc53/docsgpt-7b-mistral with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Arc53/docsgpt-7b-mistral"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Arc53/docsgpt-7b-mistral",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Arc53/docsgpt-7b-mistral

SGLang

How to use Arc53/docsgpt-7b-mistral 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 "Arc53/docsgpt-7b-mistral" \
    --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": "Arc53/docsgpt-7b-mistral",
		"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 "Arc53/docsgpt-7b-mistral" \
        --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": "Arc53/docsgpt-7b-mistral",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Arc53/docsgpt-7b-mistral with Docker Model Runner:
```
docker model run hf.co/Arc53/docsgpt-7b-mistral
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

DocsGPT is optimized for Documentation (RAG optimised): Specifically fine-tuned for providing answers that are based on context, making it particularly useful for developers and technical support teams. We used the Lora fine tuning process. This model is fine tuned on top of zephyr-7b-beta

It's an apache-2.0 license so you can use it for commercial purposes too.

Benchmarks:

Bacon: The BACON test is an internal assessment designed to evaluate the capabilities of neural networks in handling questions with substantial content. It focuses on testing the model's understanding of context-driven queries, as well as its tendency for hallucination and attention span. The questions in both parts are carefully crafted, drawing from diverse sources such as scientific papers, complex code problems, and instructional prompts, providing a comprehensive test of the model's ability to process and generate information in various domains.

Model	Score
gpt-4	8.74
DocsGPT-7b-Mistral	8.64
gpt-3.5-turbo	8.42
zephyr-7b-beta	8.37
neural-chat-7b-v3-1	7.88
Mistral-7B-Instruct-v0.1	7.44
openinstruct-mistral-7b	5.86
llama-2-13b	2.29

MTbench with llm judge:

########## First turn ##########

Model	Turn	Score
gpt-4	1	8.956250
gpt-3.5-turbo	1	8.075000
DocsGPT-7b-Mistral	1	7.593750
zephyr-7b-beta	1	7.412500
vicuna-13b-v1.3	1	6.812500
alpaca-13b	1	4.975000
deepseek-coder-6.7b	1	4.506329

########## Second turn ##########

Model	Turn	Score
gpt-4	2	9.025000
gpt-3.5-turbo	2	7.812500
DocsGPT-7b-Mistral	2	6.740000
zephyr-7b-beta	2	6.650000
vicuna-13b-v1.3	2	5.962500
deepseek-coder-6.7b	2	5.025641
alpaca-13b	2	4.087500

########## Average ##########

Model	Score
gpt-4	8.990625
gpt-3.5-turbo	7.943750
DocsGPT-7b-Mistral	7.166875
zephyr-7b-beta	7.031250
vicuna-13b-v1.3	6.387500
deepseek-coder-6.7b	4.764331
alpaca-13b	4.531250

To prepare your prompts make sure you keep this format:

### Instruction
(where the question goes)
### Context
(your document retrieval + system instructions)
### Answer

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Safetensors

Model size

7B params

Tensor type

F16

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Merges

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Quantizations

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