Instructions to use dvijay/mistral-alpaca-qlora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dvijay/mistral-alpaca-qlora with Transformers:

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

pipe = pipeline("text-generation", model="dvijay/mistral-alpaca-qlora")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dvijay/mistral-alpaca-qlora")
model = AutoModelForCausalLM.from_pretrained("dvijay/mistral-alpaca-qlora")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use dvijay/mistral-alpaca-qlora with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/dvijay/mistral-alpaca-qlora

SGLang

How to use dvijay/mistral-alpaca-qlora 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 "dvijay/mistral-alpaca-qlora" \
    --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": "dvijay/mistral-alpaca-qlora",
		"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 "dvijay/mistral-alpaca-qlora" \
        --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": "dvijay/mistral-alpaca-qlora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use dvijay/mistral-alpaca-qlora with Docker Model Runner:
```
docker model run hf.co/dvijay/mistral-alpaca-qlora
```

mistral-alpaca-qlora

This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the mhenrichsen/alpaca_2k_test dataset. It achieves the following results on the evaluation set:

Loss: 1.3095

Model description

Standard mistral 7B fine tuned with alpaca format.

Intended uses & limitations

More information needed

Training and evaluation data

mhenrichsen/alpaca_2k_test

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 128
eval_batch_size: 128
seed: 42
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
5.5317	0.07	1	5.2182
5.438	0.2	3	4.7897
4.1476	0.4	6	3.4313
3.2037	0.6	9	2.8663
2.7895	0.8	12	2.5112
2.3139	1.0	15	2.1467
2.1672	1.2	18	1.8620
1.9095	1.4	21	1.6519
1.5397	1.6	24	1.5429
1.6327	1.8	27	1.4518
1.3676	2.0	30	1.3892
1.3906	2.2	33	1.3531
1.4096	2.4	36	1.3314
1.3278	2.6	39	1.3165
1.3007	2.8	42	1.3107
1.2848	3.0	45	1.3095

Framework versions

Transformers 4.35.2
Pytorch 2.0.1+cu118
Datasets 2.15.0
Tokenizers 0.15.0

Downloads last month: 7

Safetensors

Model size

7B params

Tensor type

F16

Model tree for dvijay/mistral-alpaca-qlora

Base model

mistralai/Mistral-7B-v0.1

Finetuned

(1033)

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