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
```

ShengnanAn commited on Apr 15, 2024

Commit

06e689b

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1 Parent(s): 0a65ad8

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@@ -1,3 +1,37 @@
 ---
 license: apache-2.0
 ---

 ---
 license: apache-2.0
+datasets:
+- In2Training/VaLProbing-32K
+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/xxx" target="_blank">[LongAlign Paper]</a> • 🤗 <a href="https://huggingface.co/datasets/In2Training/VaLProbing-32K" target="_blank">[VaL Probing] </a>
+</p>
+**FILM-7B** is a 32K-context LLM that overcomes the lost-in-the-middle problem on [VaLProbing-32K](https://huggingface.co/datasets/In2Training/VaLProbing-32K/).
+It is trained from Mistral-7B-Instruct-v0.2 by applying Information-Intensie (In2) Training.
+FILM-7B achieves 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