Instructions to use Locutusque/lr-experiment1-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Locutusque/lr-experiment1-7B with Transformers:

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

pipe = pipeline("text-generation", model="Locutusque/lr-experiment1-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Locutusque/lr-experiment1-7B")
model = AutoModelForCausalLM.from_pretrained("Locutusque/lr-experiment1-7B")
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

vLLM

How to use Locutusque/lr-experiment1-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Locutusque/lr-experiment1-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Locutusque/lr-experiment1-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Locutusque/lr-experiment1-7B

SGLang

How to use Locutusque/lr-experiment1-7B 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 "Locutusque/lr-experiment1-7B" \
    --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": "Locutusque/lr-experiment1-7B",
		"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 "Locutusque/lr-experiment1-7B" \
        --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": "Locutusque/lr-experiment1-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Locutusque/lr-experiment1-7B with Docker Model Runner:
```
docker model run hf.co/Locutusque/lr-experiment1-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

lr-experiment1-7B

The lr-experiment model series is a research project I'm conducting that I will be using to determine the best learning rate to use while fine-tuning Mistral. This model uses a learning rate of 2e-5 with a cosine scheduler and no warmup steps.

I used Locutusque/Hercules-2.0-Mistral-7B as a base model, and further fine-tuned it on CollectiveCognition/chats-data-2023-09-22 using QLoRA for 3 epochs. I will be keeping track of evaluation results, and will comparing it to upcoming models.

Evals

Tasks	Version	Filter	n-shot	Metric	Value		Stderr
agieval_nous	N/A	none	None	acc	0.3645	±	0.0093
		none	None	acc_norm	0.3468	±	0.0092
- agieval_aqua_rat	1	none	None	acc	0.2283	±	0.0264
		none	None	acc_norm	0.2283	±	0.0264
- agieval_logiqa_en	1	none	None	acc	0.2965	±	0.0179
		none	None	acc_norm	0.3303	±	0.0184
- agieval_lsat_ar	1	none	None	acc	0.2217	±	0.0275
		none	None	acc_norm	0.1783	±	0.0253
- agieval_lsat_lr	1	none	None	acc	0.4039	±	0.0217
		none	None	acc_norm	0.3686	±	0.0214
- agieval_lsat_rc	1	none	None	acc	0.4870	±	0.0305
		none	None	acc_norm	0.4424	±	0.0303
- agieval_sat_en	1	none	None	acc	0.6408	±	0.0335
		none	None	acc_norm	0.5971	±	0.0343
- agieval_sat_en_without_passage	1	none	None	acc	0.3932	±	0.0341
		none	None	acc_norm	0.3835	±	0.0340
- agieval_sat_math	1	none	None	acc	0.3455	±	0.0321
		none	None	acc_norm	0.2727	±	0.0301

Groups	Version	Filter	n-shot	Metric	Value		Stderr
agieval_nous	N/A	none	None	acc	0.3645	±	0.0093
		none	None	acc_norm	0.3468	±	0.0092

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Safetensors

Model size

7B params

Tensor type

F16

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Quantizations

3 models

Locutusque
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lr-experiment1-7B

lr-experiment1-7B

Evals

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