Instructions to use tsavage68/400STEPS_5e7rate_03beta_DPO_Meditron7B_zeroshot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tsavage68/400STEPS_5e7rate_03beta_DPO_Meditron7B_zeroshot with Transformers:

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

pipe = pipeline("text-generation", model="tsavage68/400STEPS_5e7rate_03beta_DPO_Meditron7B_zeroshot")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("tsavage68/400STEPS_5e7rate_03beta_DPO_Meditron7B_zeroshot")
model = AutoModelForMultimodalLM.from_pretrained("tsavage68/400STEPS_5e7rate_03beta_DPO_Meditron7B_zeroshot")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tsavage68/400STEPS_5e7rate_03beta_DPO_Meditron7B_zeroshot with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tsavage68/400STEPS_5e7rate_03beta_DPO_Meditron7B_zeroshot"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tsavage68/400STEPS_5e7rate_03beta_DPO_Meditron7B_zeroshot",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/tsavage68/400STEPS_5e7rate_03beta_DPO_Meditron7B_zeroshot

SGLang

How to use tsavage68/400STEPS_5e7rate_03beta_DPO_Meditron7B_zeroshot 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 "tsavage68/400STEPS_5e7rate_03beta_DPO_Meditron7B_zeroshot" \
    --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": "tsavage68/400STEPS_5e7rate_03beta_DPO_Meditron7B_zeroshot",
		"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 "tsavage68/400STEPS_5e7rate_03beta_DPO_Meditron7B_zeroshot" \
        --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": "tsavage68/400STEPS_5e7rate_03beta_DPO_Meditron7B_zeroshot",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use tsavage68/400STEPS_5e7rate_03beta_DPO_Meditron7B_zeroshot with Docker Model Runner:
```
docker model run hf.co/tsavage68/400STEPS_5e7rate_03beta_DPO_Meditron7B_zeroshot
```

400STEPS_5e7rate_03beta_DPO_Meditron7B

This model is a fine-tuned version of epfl-llm/meditron-7b on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.6439
Rewards/chosen: -0.0166
Rewards/rejected: -0.1472
Rewards/accuracies: 0.5714
Rewards/margins: 0.1306
Logps/rejected: -28.2845
Logps/chosen: -26.5367
Logits/rejected: -0.6342
Logits/chosen: -0.6341

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-07
train_batch_size: 4
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
training_steps: 400

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.6896	0.1	50	0.6916	0.0067	0.0033	0.4637	0.0034	-27.7828	-26.4590	-0.6113	-0.6111
0.6783	0.2	100	0.6771	-0.0693	-0.1071	0.5319	0.0378	-28.1508	-26.7125	-0.6173	-0.6171
0.6697	0.29	150	0.6571	-0.0107	-0.1001	0.5626	0.0893	-28.1273	-26.5172	-0.6171	-0.6170
0.6463	0.39	200	0.6496	0.0037	-0.1067	0.5692	0.1104	-28.1493	-26.4691	-0.6288	-0.6286
0.6124	0.49	250	0.6449	-0.0073	-0.1329	0.5648	0.1257	-28.2368	-26.5056	-0.6318	-0.6317
0.641	0.59	300	0.6440	-0.0156	-0.1460	0.5758	0.1304	-28.2803	-26.5333	-0.6340	-0.6339
0.643	0.68	350	0.6430	-0.0150	-0.1479	0.5780	0.1328	-28.2866	-26.5315	-0.6343	-0.6341
0.6632	0.78	400	0.6439	-0.0166	-0.1472	0.5714	0.1306	-28.2845	-26.5367	-0.6342	-0.6341

Framework versions

Transformers 4.37.2
Pytorch 2.0.0+cu117
Datasets 2.17.0
Tokenizers 0.15.1

Downloads last month: 8

Safetensors

Model size

7B params

Tensor type

F16

Model tree for tsavage68/400STEPS_5e7rate_03beta_DPO_Meditron7B_zeroshot

Base model

meta-llama/Llama-2-7b

Finetuned

epfl-llm/meditron-7b

Finetuned

(41)

this model