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

Libraries

How to use GritLM/GritLM-7B with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GritLM/GritLM-7B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("GritLM/GritLM-7B", trust_remote_code=True)
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use GritLM/GritLM-7B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/GritLM/GritLM-7B

SGLang

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

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

Model Summary

GritLM is a generative representational instruction tuned language model. It unifies text representation (embedding) and text generation into a single model achieving state-of-the-art performance on both types of tasks.

Repository: ContextualAI/gritlm
Paper: https://arxiv.org/abs/2402.09906
Logs: https://wandb.ai/muennighoff/gritlm/runs/0uui712t/overview
Script: https://github.com/ContextualAI/gritlm/blob/main/scripts/training/train_gritlm_7b.sh

Model	Description
GritLM 7B	Mistral 7B finetuned using GRIT
GritLM 8x7B	Mixtral 8x7B finetuned using GRIT

Use

The model usage is documented here.

Citation

@misc{muennighoff2024generative,
      title={Generative Representational Instruction Tuning}, 
      author={Niklas Muennighoff and Hongjin Su and Liang Wang and Nan Yang and Furu Wei and Tao Yu and Amanpreet Singh and Douwe Kiela},
      year={2024},
      eprint={2402.09906},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Downloads last month: 13,556

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for GritLM/GritLM-7B

Base model

mistralai/Mistral-7B-v0.1

Finetuned

(1032)

this model

Adapters

Finetunes

Merges

Quantizations

Dataset used to train GritLM/GritLM-7B

Spaces using GritLM/GritLM-7B 48

Collection including GritLM/GritLM-7B

GritLM

Collection

Generative Representational Instruction Tuning (GRIT) • 63 items • Updated Mar 2 • 9

Paper for GritLM/GritLM-7B

Generative Representational Instruction Tuning

Paper • 2402.09906 • Published Feb 15, 2024 • 54

Evaluation results

ArguAna Default Test on mteb/arguana View evaluation results

source leaderboard

63.17 ^*
ArguAna on mteb/arguana View evaluation results

source leaderboard

63.17 ^*

accuracy on MTEB AmazonCounterfactualClassification (en)
test set self-reported

81.179
ap on MTEB AmazonCounterfactualClassification (en)
test set self-reported

46.263
f1 on MTEB AmazonCounterfactualClassification (en)
test set self-reported

75.446
accuracy on MTEB AmazonPolarityClassification
test set self-reported

96.516
ap on MTEB AmazonPolarityClassification
test set self-reported

94.791
f1 on MTEB AmazonPolarityClassification
test set self-reported

96.515