Instructions to use GritLM/GritLM-8x7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GritLM/GritLM-8x7B with Transformers:

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

pipe = pipeline("text-generation", model="GritLM/GritLM-8x7B", 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-8x7B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("GritLM/GritLM-8x7B", trust_remote_code=True, 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 GritLM/GritLM-8x7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GritLM/GritLM-8x7B"
# 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-8x7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GritLM/GritLM-8x7B

SGLang

How to use GritLM/GritLM-8x7B 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-8x7B" \
    --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-8x7B",
		"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-8x7B" \
        --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-8x7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GritLM/GritLM-8x7B with Docker Model Runner:
```
docker model run hf.co/GritLM/GritLM-8x7B
```

Muennighoff commited on Feb 13, 2024

Commit

83b89f8

verified ·

1 Parent(s): 9a00d10

Update README.md

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README.md +10 -25

README.md CHANGED Viewed

@@ -2,6 +2,8 @@
 pipeline_tag: text-generation
 inference: true
 license: apache-2.0
 ---
 # Table of Contents
@@ -13,39 +15,22 @@ license: apache-2.0
 # Model Summary
-> GritLM is a generative-representational instruction-tuned language model. It performs well at both text representation and text generation.
 - **Repository:** [ContextualAI/gritlm](https://github.com/ContextualAI/gritlm)
 - **Paper:** [TODO](https://arxiv.org/abs/2308.07124)
-# Use
-The models usage is documented [here](TODO). It supports GritLM, Transformers, Sentence Transformers.
-# Training
-## Model
-- **Architecture:** Mistral-8x7B
-- **Steps:** 250k pretraining & 30 instruction tuning
-- **Pretraining tokens:** ? pretraining & 2M instruction tuning
-- **Precision:** bfloat16
-## Hardware
-- **Pretraining:**
-  - **GPUs:** 512 Tesla A100
-  - **Training time:** 1 day
-- **Instruction tuning:**
-  - **GPUs:** 8 Tesla A100
-  - **Training time:** 4 hours
-## Software
-https://github.com/ContextualAI/gritlm
 # Citation
 ```bibtex
 TODO
-```

 pipeline_tag: text-generation
 inference: true
 license: apache-2.0
+datasets:
+- GritLM/tulu2
 ---
 # Table of Contents
 # 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](https://github.com/ContextualAI/gritlm)
 - **Paper:** [TODO](https://arxiv.org/abs/2308.07124)
+| Model | Description |
+|-------|-------------|
+| [GritLM 7B](https://hf.co/GritLM/GritLM-7B) | Mistral 7B finetuned using GRIT |
+| [GritLM 8x7B](https://hf.co/GritLM/GritLM-8x7B) | Mixtral 8x7B finetuned using GRIT |
+# Use
+The model usage is documented [here](TODO). It supports GritLM, Transformers, Sentence Transformers.
 # Citation
 ```bibtex
 TODO
+```