Instructions to use In2Training/FILM-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use In2Training/FILM-7B with Transformers:

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

pipe = pipeline("text-generation", model="In2Training/FILM-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("In2Training/FILM-7B")
model = AutoModelForCausalLM.from_pretrained("In2Training/FILM-7B", 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 In2Training/FILM-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "In2Training/FILM-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": "In2Training/FILM-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/In2Training/FILM-7B

SGLang

How to use In2Training/FILM-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 "In2Training/FILM-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": "In2Training/FILM-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 "In2Training/FILM-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": "In2Training/FILM-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use In2Training/FILM-7B with Docker Model Runner:
```
docker model run hf.co/In2Training/FILM-7B
```

FILM-7B / README.md

ShengnanAn

Update README.md

beef182 verified about 2 years ago

preview code

Raw

History Blame Contribute Delete

2.35 kB

	---
	license: apache-2.0
	language:
	- en
	---

	# FILM-7B

	<p align="center">
	💻 <a href="https://github.com/microsoft/FILM/" target="_blank">[Github Repo]</a> • 📃 <a href="https://arxiv.org/abs/2404.16811" target="_blank">[Paper]</a> • ⚓ <a href="https://huggingface.co/datasets/In2Training/VaLProbing-32K" target="_blank">[VaLProbing-32K] </a>
	</p>

	FILM-7B is a 32K-context LLM that overcomes the lost-in-the-middle problem.
	It is trained from Mistral-7B-Instruct-v0.2 by applying Information-Intensie (In2) Training.
	FILM-7B achieves near-perfect performance on probing tasks, SOTA-level performance on real-world long-context tasks among ~7B size LLMs, and does not compromise the short-context performance.

	## Model Usage

	The system tempelate for FILM-7B:
	```text
	'''[INST] Below is a context and an instruction. Based on the information provided in the context, write a response for the instruction.

	### Context:
	{YOUR LONG CONTEXT}

	### Instruction:
	{YOUR QUESTION & INSTRUCTION} [/INST]
	'''
	```

	## Probing Results

	To reproduce the results on our VaL Probing, see the guidance in [https://github.com/microsoft/FILM/tree/main/VaLProbing](https://github.com/microsoft/FILM/tree/main/VaLProbing).

	<p align="center">
	<img src="./figures/probing_results_new.png" width="800">
	<br>
	</p>

	## Real-World Long-Context Tasks

	To reproduce the results on real-world long-context tasks, see the guidance in [https://github.com/microsoft/FILM/tree/main/real_world_long](https://github.com/microsoft/FILM/tree/main/real_world_long).

	<p align="center">
	<img src="./figures/real_world_long.png" width="800">
	<br>
	</p>

	## Short-Context Tasks

	To reproduce the results on short-context tasks, see the guidance in [https://github.com/microsoft/FILM/tree/main/short_tasks](https://github.com/microsoft/FILM/tree/main/short_tasks).

	<p align="center">
	<img src="./figures/short.png" width="800">
	<br>
	</p>

	## 📝 Citation
	```
	@misc{an2024make,
	title={Make Your LLM Fully Utilize the Context},
	author={Shengnan An and Zexiong Ma and Zeqi Lin and Nanning Zheng and Jian-Guang Lou},
	year={2024},
	eprint={2404.16811},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	```

	Disclaimer: This model is strictly for research purposes, and not an official product or service from Microsoft.