Instructions to use athirdpath/BigMistral-11b-GLUE_LORA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use athirdpath/BigMistral-11b-GLUE_LORA with Transformers:

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

pipe = pipeline("text-generation", model="athirdpath/BigMistral-11b-GLUE_LORA")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("athirdpath/BigMistral-11b-GLUE_LORA")
model = AutoModelForCausalLM.from_pretrained("athirdpath/BigMistral-11b-GLUE_LORA")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use athirdpath/BigMistral-11b-GLUE_LORA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "athirdpath/BigMistral-11b-GLUE_LORA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "athirdpath/BigMistral-11b-GLUE_LORA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/athirdpath/BigMistral-11b-GLUE_LORA

SGLang

How to use athirdpath/BigMistral-11b-GLUE_LORA 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 "athirdpath/BigMistral-11b-GLUE_LORA" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "athirdpath/BigMistral-11b-GLUE_LORA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "athirdpath/BigMistral-11b-GLUE_LORA" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "athirdpath/BigMistral-11b-GLUE_LORA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use athirdpath/BigMistral-11b-GLUE_LORA with Docker Model Runner:
```
docker model run hf.co/athirdpath/BigMistral-11b-GLUE_LORA
```

Regret: Should have not targeted Q, V, K, O; as those are less impactful for "healing" but more impactful on performance otherwise. Still works great!

qlora

This model is a fine-tuned version of athirdpath/BigMistral-11b on the athirdpath/Merge_Glue dataset. It achieves the following results on the evaluation set:

Loss: 0.9174

Before and After Example

Example model is athirdpath/CleverMage-11b

Example with LoRA (min_p, alpaca)

Example without LoRA (min_p, chatML)

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0005
train_batch_size: 10
eval_batch_size: 10
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 40
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
1.2198	0.63	30	0.9055
1.1206	1.26	60	0.8951
1.1319	1.89	90	0.8904
1.0031	2.51	120	0.9174

Framework versions

Transformers 4.35.2
Pytorch 2.0.1+cu118
Datasets 2.15.0
Tokenizers 0.15.0

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