Instructions to use iblai/ibl-fordham-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use iblai/ibl-fordham-7b with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("iblai/ibl-fordham-7b")
model = AutoModelForCausalLM.from_pretrained("iblai/ibl-fordham-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 iblai/ibl-fordham-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "iblai/ibl-fordham-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": "iblai/ibl-fordham-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/iblai/ibl-fordham-7b

SGLang

How to use iblai/ibl-fordham-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 "iblai/ibl-fordham-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": "iblai/ibl-fordham-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 "iblai/ibl-fordham-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": "iblai/ibl-fordham-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use iblai/ibl-fordham-7b with Docker Model Runner:
```
docker model run hf.co/iblai/ibl-fordham-7b
```

ibleducation/ibl-fordham-7b

ibleducation/ibl-fordham-7b is a model finetuned on top of openchat/openchat_3.5

This model is finetuned to answer questions about fordham university.

Model Details

Developed by: IBL Education
Model type: Mistral-7B-v0.1
Base Model: OpenChat 3.5
Language: English
Finetuned from weights: OpenChat 3.5
Finetuned on data:
- ibleducation/fordham-university
Model License: Apache 2.0
Epochs: 7

How to Use ibl-fordham-7b Model from Python Code (HuggingFace transformers)

Install the necessary packages

Requires: transformers 4.35.0 or later, and accelerate 0.23.0 or later.

pip install transformers==4.35.0
pip install accelerate==0.23.0

You can then try the following example code

from transformers import AutoModelForCausalLM, AutoTokenizer
import transformers
import torch

model_id = "ibleducation/ibl-fordham-7b"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
  model_id,
  device_map="auto",
)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
)
prompt = "<s>What programmes are offered at fordham university?</s>"

response = pipeline(prompt)
print(response['generated_text'])

Important - Use the prompt template below for ibl-fordham-7b:

<s>{prompt}</s>

Downloads last month: 7

Safetensors

Model size

7B params

Tensor type

F16

iblai
/

ibl-fordham-7b

ibleducation/ibl-fordham-7b

Model Details

How to Use ibl-fordham-7b Model from Python Code (HuggingFace transformers)

Install the necessary packages

You can then try the following example code

Dataset used to train iblai/ibl-fordham-7b