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

Libraries

How to use NECOUDBFM/Jellyfish-7B with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NECOUDBFM/Jellyfish-7B")
model = AutoModelForCausalLM.from_pretrained("NECOUDBFM/Jellyfish-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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NECOUDBFM/Jellyfish-7B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/NECOUDBFM/Jellyfish-7B

SGLang

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

Docker Model Runner
How to use NECOUDBFM/Jellyfish-7B with Docker Model Runner:
```
docker model run hf.co/NECOUDBFM/Jellyfish-7B
```

yuyangdong commited on Jun 25, 2024

Commit

b2f528d

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1 Parent(s): 24fc6a3

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@@ -13,10 +13,9 @@ language:
 ## Model Details
 Jellyfish-7B is a large language model equipped with 7 billion parameters.
-We fine-tuned the [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) model using the datasets pertinent to data preprocessing tasks.
-The training data include two parts:
-* Jellyfish-13B training data
-* GPT4 generated reasoning data for data preprocessing tasks.
 Jellyfish-7B vs GPT-3.5-turbo wining rate by GPT4 evaluation is 56.36%.

 ## Model Details
 Jellyfish-7B is a large language model equipped with 7 billion parameters.
+We fine-tuned the [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) model using
+apart of the [Jellyfish-Instruct](https://huggingface.co/datasets/NECOUDBFM/Jellyfish-Instruct)
 Jellyfish-7B vs GPT-3.5-turbo wining rate by GPT4 evaluation is 56.36%.