Instructions to use m-a-p/neo_7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use m-a-p/neo_7b with Transformers:

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

pipe = pipeline("text-generation", model="m-a-p/neo_7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("m-a-p/neo_7b")
model = AutoModelForCausalLM.from_pretrained("m-a-p/neo_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 m-a-p/neo_7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "m-a-p/neo_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": "m-a-p/neo_7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/m-a-p/neo_7b

SGLang

How to use m-a-p/neo_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 "m-a-p/neo_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": "m-a-p/neo_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 "m-a-p/neo_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": "m-a-p/neo_7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use m-a-p/neo_7b with Docker Model Runner:
```
docker model run hf.co/m-a-p/neo_7b
```

dododododo commited on Jun 3, 2024

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@@ -12,6 +12,8 @@ Neo is a completely open source large language model, including code, all model
 | Model | Describe | Download |
 |---|---|---|
 neo_7b| This repository contains the base model of neo_7b  | • [🤗 Hugging Face](https://huggingface.co/m-a-p/neo_7b)
 neo_7b_intermediate| This repo contains normal pre-training intermediate ckpts. A total of 3.7T tokens were learned at this phase. | • [🤗 Hugging Face](https://huggingface.co/m-a-p/neo_7b_intermediate)
 neo_7b_decay| This repo contains intermediate ckpts during the decay phase. A total of 720B tokens were learned at this phase.  | • [🤗 Hugging Face](https://huggingface.co/m-a-p/neo_7b_decay)
 neo_scalinglaw_980M | This repo contains ckpts related to scalinglaw experiments | • [🤗 Hugging Face](https://huggingface.co/m-a-p/neo_scalinglaw_980M)

 | Model | Describe | Download |
 |---|---|---|
 neo_7b| This repository contains the base model of neo_7b  | • [🤗 Hugging Face](https://huggingface.co/m-a-p/neo_7b)
+neo_7b_sft_v0.1| This repository contains the supervised fine-tuning version of the neo_7b model.  | • [🤗 Hugging Face](https://huggingface.co/m-a-p/neo_7b_sft_v0.1)
+neo_7b_instruct_v0.1| This repository contains the instruction-tuned version of the neo_7b model.  | • [🤗 Hugging Face](https://huggingface.co/m-a-p/neo_7b_instruct_v0.1)
 neo_7b_intermediate| This repo contains normal pre-training intermediate ckpts. A total of 3.7T tokens were learned at this phase. | • [🤗 Hugging Face](https://huggingface.co/m-a-p/neo_7b_intermediate)
 neo_7b_decay| This repo contains intermediate ckpts during the decay phase. A total of 720B tokens were learned at this phase.  | • [🤗 Hugging Face](https://huggingface.co/m-a-p/neo_7b_decay)
 neo_scalinglaw_980M | This repo contains ckpts related to scalinglaw experiments | • [🤗 Hugging Face](https://huggingface.co/m-a-p/neo_scalinglaw_980M)