Instructions to use ajibawa-2023/WikiHow-Mistral-Instruct-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ajibawa-2023/WikiHow-Mistral-Instruct-7B with Transformers:

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

pipe = pipeline("text-generation", model="ajibawa-2023/WikiHow-Mistral-Instruct-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ajibawa-2023/WikiHow-Mistral-Instruct-7B")
model = AutoModelForCausalLM.from_pretrained("ajibawa-2023/WikiHow-Mistral-Instruct-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 Settings

vLLM

How to use ajibawa-2023/WikiHow-Mistral-Instruct-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ajibawa-2023/WikiHow-Mistral-Instruct-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": "ajibawa-2023/WikiHow-Mistral-Instruct-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ajibawa-2023/WikiHow-Mistral-Instruct-7B

SGLang

How to use ajibawa-2023/WikiHow-Mistral-Instruct-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 "ajibawa-2023/WikiHow-Mistral-Instruct-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": "ajibawa-2023/WikiHow-Mistral-Instruct-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 "ajibawa-2023/WikiHow-Mistral-Instruct-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": "ajibawa-2023/WikiHow-Mistral-Instruct-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ajibawa-2023/WikiHow-Mistral-Instruct-7B with Docker Model Runner:
```
docker model run hf.co/ajibawa-2023/WikiHow-Mistral-Instruct-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

WikiHow-Mistral-Instruct-7B

This Model is trained on my WikiHow dataset.

This model is very very good with generating tutorials in the style of WikiHow. By leveraging this repository of practical knowledge, the model has been trained to comprehend and generate text that is highly informative and instructional in nature. The depth and accuracy of generated tutorials is exceptional. The WikiHow dataset encompasses a wide array of topics, ranging from everyday tasks to specialized skills, making it an invaluable resource for refining the capabilities of language models. Through this fine-tuning process, the model has been equipped with the ability to offer insightful guidance and assistance across diverse domains.

This is a fully finetuned model. Links for Quantized models are given below.

GGUF & Exllama

GGUF: Link

Exllama v2: Link

Special Thanks to Bartowski for quantizing this model.

Training:

Entire dataset was trained on 4 x A100 80GB. For 3 epoch, training took more than 15 Hours. Axolotl codebase was used for training purpose. Entire data is trained on Mistral-7B-Instruct-v0.2.

Example Prompt:

This model uses ChatML prompt format.

<|im_start|>system
You are a Helpful Assistant who can write long and very detailed tutorial on various subjects in the styles of WiKiHow. Include in depth explanations for each step and how it helps achieve the desired outcome, including key tips and guidelines.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

You can modify above Prompt as per your requirement.

I want to say special Thanks to the Open Source community for helping & guiding me to better understand the AI/Model development.

Thank you for your love & support.

Example Output

Example 1

Example 2

Example 3

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	61.25
AI2 Reasoning Challenge (25-Shot)	60.92
HellaSwag (10-Shot)	80.99
MMLU (5-Shot)	58.57
TruthfulQA (0-shot)	62.16
Winogrande (5-shot)	74.82
GSM8k (5-shot)	30.02

Downloads last month: 747

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for ajibawa-2023/WikiHow-Mistral-Instruct-7B

Quantizations

1 model

Spaces using ajibawa-2023/WikiHow-Mistral-Instruct-7B 8

Evaluation results

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

60.920
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

80.990
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

58.570
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

62.160
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

74.820
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

30.020