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")
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 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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

FILM-7B

💻 [Github Repo] • 📃 [Paper] • ⚓ [VaLProbing-32K]

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:

'''[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.

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.

Short-Context Tasks

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

📝 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.

Downloads last month: 60

Safetensors

Model size

7B params

Tensor type

F32

Model tree for In2Training/FILM-7B

Finetunes

3 models

Quantizations

2 models

Paper for In2Training/FILM-7B

Make Your LLM Fully Utilize the Context

Paper • 2404.16811 • Published Apr 25, 2024 • 55