Instructions to use instructlab/merlinite-7b-pt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use instructlab/merlinite-7b-pt with Transformers:

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

pipe = pipeline("text-generation", model="instructlab/merlinite-7b-pt")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("instructlab/merlinite-7b-pt")
model = AutoModelForCausalLM.from_pretrained("instructlab/merlinite-7b-pt", 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 instructlab/merlinite-7b-pt with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "instructlab/merlinite-7b-pt"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "instructlab/merlinite-7b-pt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/instructlab/merlinite-7b-pt

SGLang

How to use instructlab/merlinite-7b-pt 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 "instructlab/merlinite-7b-pt" \
    --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": "instructlab/merlinite-7b-pt",
		"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 "instructlab/merlinite-7b-pt" \
        --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": "instructlab/merlinite-7b-pt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use instructlab/merlinite-7b-pt with Docker Model Runner:
```
docker model run hf.co/instructlab/merlinite-7b-pt
```

gx-ai-architect commited on Jun 24, 2024

Commit

80a24c4

verified ·

1 Parent(s): f4ff397

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -18,7 +18,7 @@ base_model: mistralai/Mistral-7B-v0.1
 # Model Card for Merlinite-7B-pt 🔥
 ### Overview
-We introduce **Merlinite-7B-pt**, a strong open-source chat model, aligned using AI feedback **without proprietary models or using any human annotation**.
 - **Merlinite-7B-pt** is first supervised-finetuned (SFT) via [LAB](https://arxiv.org/abs/2403.01081) using Mistral-7B-v0.1 as base model, and then preference-tuned via AI feedback.
 - Our preference tuning recipe uses the DPO reward from Mixtral-8x7B-Instruct-v0.1 as the proxy for human preferences, and applies iterative rejection sampling to finetune the SFT policy.
 - We show that DPO log-ratios can serve as a reliable reward signal, showing clear correlation between reward improvements and MT-Bench improvements.

 # Model Card for Merlinite-7B-pt 🔥
 ### Overview
+We introduce **Merlinite-7B-pt**, a strong open-source chat model, preference aligned using AI feedback **without proprietary models or using any human annotation**.
 - **Merlinite-7B-pt** is first supervised-finetuned (SFT) via [LAB](https://arxiv.org/abs/2403.01081) using Mistral-7B-v0.1 as base model, and then preference-tuned via AI feedback.
 - Our preference tuning recipe uses the DPO reward from Mixtral-8x7B-Instruct-v0.1 as the proxy for human preferences, and applies iterative rejection sampling to finetune the SFT policy.
 - We show that DPO log-ratios can serve as a reliable reward signal, showing clear correlation between reward improvements and MT-Bench improvements.