Instructions to use cortecs/Mistral-7B-Instruct-v0.3-GPTQ-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cortecs/Mistral-7B-Instruct-v0.3-GPTQ-8b with Transformers:

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

pipe = pipeline("text-generation", model="cortecs/Mistral-7B-Instruct-v0.3-GPTQ-8b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cortecs/Mistral-7B-Instruct-v0.3-GPTQ-8b")
model = AutoModelForCausalLM.from_pretrained("cortecs/Mistral-7B-Instruct-v0.3-GPTQ-8b", device_map="auto")
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 Settings

vLLM

How to use cortecs/Mistral-7B-Instruct-v0.3-GPTQ-8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cortecs/Mistral-7B-Instruct-v0.3-GPTQ-8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cortecs/Mistral-7B-Instruct-v0.3-GPTQ-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cortecs/Mistral-7B-Instruct-v0.3-GPTQ-8b

SGLang

How to use cortecs/Mistral-7B-Instruct-v0.3-GPTQ-8b 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 "cortecs/Mistral-7B-Instruct-v0.3-GPTQ-8b" \
    --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": "cortecs/Mistral-7B-Instruct-v0.3-GPTQ-8b",
		"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 "cortecs/Mistral-7B-Instruct-v0.3-GPTQ-8b" \
        --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": "cortecs/Mistral-7B-Instruct-v0.3-GPTQ-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cortecs/Mistral-7B-Instruct-v0.3-GPTQ-8b with Docker Model Runner:
```
docker model run hf.co/cortecs/Mistral-7B-Instruct-v0.3-GPTQ-8b
```

This is a quantized model of Mistral-7B-Instruct-v0.3 using GPTQ developed by IST Austria using the following configuration:

8bit
Act order: True
Group size: 128

Usage

Install vLLM and run the server:

python -m vllm.entrypoints.openai.api_server --model cortecs/Mistral-7B-Instruct-v0.3-GPTQ-8b

Access the model:

curl http://localhost:8000/v1/completions     -H "Content-Type: application/json"     -d ' {
        "model": "cortecs/Mistral-7B-Instruct-v0.3-GPTQ-8b",
        "prompt": "San Francisco is a"
    } '

Evaluations

English	Mistral-7B-Instruct-v0.3	Mistral-7B-Instruct-v0.3-GPTQ-8b	Mistral-7B-Instruct-v0.3-GPTQ-4b
Avg.	67.65	67.72	66.95
ARC	64.2	64.1	62.1
Hellaswag	75.6	75.6	76.0
MMLU	63.16	63.47	62.75

French	Mistral-7B-Instruct-v0.3	Mistral-7B-Instruct-v0.3-GPTQ-8b	Mistral-7B-Instruct-v0.3-GPTQ-4b
Avg.	56.4	56.17	54.77
ARC_fr	51.9	51.4	50.0
Hellaswag_fr	65.8	65.8	63.8
MMLU_fr	51.5	51.3	50.5

German	Mistral-7B-Instruct-v0.3	Mistral-7B-Instruct-v0.3-GPTQ-8b	Mistral-7B-Instruct-v0.3-GPTQ-4b
Avg.	51.83	51.73	51.7
ARC_de	47.6	47.5	47.3
Hellaswag_de	58.9	59.0	57.3
MMLU_de	49.0	48.7	50.5

Italian	Mistral-7B-Instruct-v0.3	Mistral-7B-Instruct-v0.3-GPTQ-8b	Mistral-7B-Instruct-v0.3-GPTQ-4b
Avg.	54.93	54.8	52.83
ARC_it	51.6	51.6	49.3
Hellaswag_it	63.5	63.8	61.0
MMLU_it	49.7	49.0	48.2

Safety	Mistral-7B-Instruct-v0.3	Mistral-7B-Instruct-v0.3-GPTQ-8b	Mistral-7B-Instruct-v0.3-GPTQ-4b
Avg.	60.32	60.54	64.8
RealToxicityPrompts	89.7	90.0	90.7
TruthfulQA	59.71	59.48	58.32
CrowS	31.54	32.14	45.38

Spanish	Mistral-7B-Instruct-v0.3	Mistral-7B-Instruct-v0.3-GPTQ-8b	Mistral-7B-Instruct-v0.3-GPTQ-4b
Avg.	57.9	57.97	56.1
ARC_es	53.5	53.5	51
Hellaswag_es	68.5	68.5	66.2
MMLU_es	51.7	51.9	51.1

We did not check for data contamination. Evaluation was done using Eval. Harness using limit=1000.

Performance

	requests/s	tokens/s
NVIDIA L4x1	2.63	1308.8
NVIDIA L4x2	4.36	2168.56
NVIDIA L4x4	5.53	2751.53
Performance measured on cortecs inference.

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cortecs
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Mistral-7B-Instruct-v0.3-GPTQ-8b

Usage

Evaluations

Performance

Dataset used to train cortecs/Mistral-7B-Instruct-v0.3-GPTQ-8b