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

Libraries

How to use monology/openinstruct-mistral-7b with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("monology/openinstruct-mistral-7b")
model = AutoModelForCausalLM.from_pretrained("monology/openinstruct-mistral-7b")
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

vLLM

How to use monology/openinstruct-mistral-7b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/monology/openinstruct-mistral-7b

SGLang

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

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

OpenInstruct Mistral-7B

1st among commercially-usable 7B models on the Open LLM Leaderboard!*

This is mistralai/Mistral-7B-v0.1 finetuned on VMware/open-instruct.
Quantized to FP16 and released under the Apache-2.0 license by myself.
Compute generously provided by Higgsfield AI.

Prompt format: Alpaca

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
[your instruction goes here]

### Response:

Recommended preset:

temperature: 0.2
top_k: 50
top_p 0.95
repetition_penalty: 1.1

*as of 21 Nov 2023. "commercially-usable" includes both an open-source base model and a non-synthetic open-source finetune dataset. updated leaderboard results available here.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	63.64
AI2 Reasoning Challenge (25-Shot)	59.73
HellaSwag (10-Shot)	82.77
MMLU (5-Shot)	60.55
TruthfulQA (0-shot)	48.76
Winogrande (5-shot)	79.56
GSM8k (5-shot)	50.49

Downloads last month: 209

Safetensors

Model size

7B params

Tensor type

F16

Model tree for monology/openinstruct-mistral-7b

Quantizations

6 models

Dataset used to train monology/openinstruct-mistral-7b

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

59.730
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

82.770
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

60.550
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

48.760
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

79.560
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

50.490