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

Libraries

How to use ReBatch/Reynaerde-7B-Instruct with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ReBatch/Reynaerde-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("ReBatch/Reynaerde-7B-Instruct")
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 ReBatch/Reynaerde-7B-Instruct with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ReBatch/Reynaerde-7B-Instruct

SGLang

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

Docker Model Runner
How to use ReBatch/Reynaerde-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/ReBatch/Reynaerde-7B-Instruct
```

vandeju commited on Jun 6, 2024

Commit

7b4d5b6

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1 Parent(s): 0afe8b8

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@@ -43,3 +43,31 @@ It achieves the following results on the evaluation set:
 ## Training procedure
 This model was trained with QLoRa in bfloat16 with Flash Attention 2 on one A100 PCIe, using the sft script from the [alignment handbook](https://github.com/huggingface/alignment-handbook/) on [RunPod](https://www.runpod.io/).

 ## Training procedure
 This model was trained with QLoRa in bfloat16 with Flash Attention 2 on one A100 PCIe, using the sft script from the [alignment handbook](https://github.com/huggingface/alignment-handbook/) on [RunPod](https://www.runpod.io/).
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 3
+- eval_batch_size: 6
+- seed: 42
+- distributed_type: multi-GPU
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 6
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 1
+### Framework versions
+- PEFT 0.11.1
+- Transformers 4.41.2
+- Pytorch 2.2.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1
+### Model Developer
+The Mistral-7B-v0.3-Instruct model, on which this model is based, was created by [Mistral AI](https://huggingface.co/mistralai).
+The finetuning was done by [Julien Van den Avenne](https://huggingface.co/vandeju).