Instructions to use tsavage68/MedQA_L3_1000steps_1e6rate_01beta_CSFTDPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tsavage68/MedQA_L3_1000steps_1e6rate_01beta_CSFTDPO with Transformers:

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

pipe = pipeline("text-generation", model="tsavage68/MedQA_L3_1000steps_1e6rate_01beta_CSFTDPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tsavage68/MedQA_L3_1000steps_1e6rate_01beta_CSFTDPO")
model = AutoModelForCausalLM.from_pretrained("tsavage68/MedQA_L3_1000steps_1e6rate_01beta_CSFTDPO", device_map="auto")
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 Settings

vLLM

How to use tsavage68/MedQA_L3_1000steps_1e6rate_01beta_CSFTDPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tsavage68/MedQA_L3_1000steps_1e6rate_01beta_CSFTDPO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tsavage68/MedQA_L3_1000steps_1e6rate_01beta_CSFTDPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tsavage68/MedQA_L3_1000steps_1e6rate_01beta_CSFTDPO

SGLang

How to use tsavage68/MedQA_L3_1000steps_1e6rate_01beta_CSFTDPO 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/MedQA_L3_1000steps_1e6rate_01beta_CSFTDPO" \
    --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": "tsavage68/MedQA_L3_1000steps_1e6rate_01beta_CSFTDPO",
		"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 "tsavage68/MedQA_L3_1000steps_1e6rate_01beta_CSFTDPO" \
        --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": "tsavage68/MedQA_L3_1000steps_1e6rate_01beta_CSFTDPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tsavage68/MedQA_L3_1000steps_1e6rate_01beta_CSFTDPO with Docker Model Runner:
```
docker model run hf.co/tsavage68/MedQA_L3_1000steps_1e6rate_01beta_CSFTDPO
```

MedQA_L3_1000steps_1e6rate_01beta_CSFTDPO

This model is a fine-tuned version of tsavage68/MedQA_L3_1000steps_1e6rate_SFT on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.4143
Rewards/chosen: -0.2461
Rewards/rejected: -2.6298
Rewards/accuracies: 0.8088
Rewards/margins: 2.3838
Logps/rejected: -60.1531
Logps/chosen: -33.7891
Logits/rejected: -1.3940
Logits/chosen: -1.3910

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: 1e-06
train_batch_size: 2
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
training_steps: 1000

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.6869	0.0489	50	0.6696	-0.2211	-0.2710	0.7253	0.0498	-36.5645	-33.5400	-0.7298	-0.7290
0.4779	0.0977	100	0.5887	1.4526	1.0417	0.6945	0.4109	-23.4374	-16.8024	-0.8047	-0.8036
0.5752	0.1466	150	0.4975	0.5331	-0.2997	0.7473	0.8328	-36.8518	-25.9976	-0.8723	-0.8705
0.4157	0.1954	200	0.5087	-0.0815	-1.0065	0.7538	0.9250	-43.9199	-32.1434	-0.9039	-0.9019
0.4271	0.2443	250	0.4619	0.5202	-0.5333	0.7648	1.0535	-39.1874	-26.1265	-0.9341	-0.9319
0.3162	0.2931	300	0.4272	0.2052	-1.3157	0.8110	1.5209	-47.0122	-29.2765	-1.0303	-1.0281
0.3868	0.3420	350	0.4366	0.0191	-1.4354	0.7868	1.4545	-48.2090	-31.1376	-1.1172	-1.1146
0.4267	0.3908	400	0.4253	0.8142	-0.6501	0.8044	1.4642	-40.3556	-23.1869	-1.2091	-1.2069
0.4816	0.4397	450	0.4235	0.7057	-0.6954	0.7978	1.4011	-40.8093	-24.2719	-1.2618	-1.2590
0.5777	0.4885	500	0.4147	0.5199	-1.2061	0.8088	1.7260	-45.9158	-26.1293	-1.3148	-1.3119
0.3051	0.5374	550	0.4133	0.2933	-1.3715	0.8022	1.6647	-47.5694	-28.3956	-1.3646	-1.3616
0.5378	0.5862	600	0.4219	-0.4403	-2.6925	0.8088	2.2522	-60.7803	-35.7319	-1.3525	-1.3496
0.359	0.6351	650	0.4122	-0.0585	-2.2242	0.8132	2.1656	-56.0965	-31.9139	-1.3793	-1.3763
0.4137	0.6839	700	0.4019	0.0561	-2.0220	0.8066	2.0781	-54.0746	-30.7675	-1.3921	-1.3890
0.3899	0.7328	750	0.4093	-0.1488	-2.4231	0.8110	2.2743	-58.0863	-32.8165	-1.3920	-1.3890
0.3645	0.7816	800	0.4095	-0.2104	-2.5505	0.8132	2.3401	-59.3594	-33.4322	-1.3965	-1.3935
0.4993	0.8305	850	0.4157	-0.2412	-2.6172	0.8088	2.3760	-60.0272	-33.7410	-1.3947	-1.3918
0.6907	0.8793	900	0.4164	-0.2462	-2.6292	0.8110	2.3829	-60.1466	-33.7908	-1.3944	-1.3914
0.3846	0.9282	950	0.4140	-0.2447	-2.6315	0.8110	2.3868	-60.1702	-33.7755	-1.3939	-1.3909
0.3404	0.9770	1000	0.4143	-0.2461	-2.6298	0.8088	2.3838	-60.1531	-33.7891	-1.3940	-1.3910

Framework versions

Transformers 4.41.1
Pytorch 2.0.0+cu117
Datasets 2.19.1
Tokenizers 0.19.1

Downloads last month: 6

Safetensors

Model size

8B params

Tensor type

F16

Model tree for tsavage68/MedQA_L3_1000steps_1e6rate_01beta_CSFTDPO

Base model

meta-llama/Meta-Llama-3-8B-Instruct

Finetuned

tsavage68/MedQA_L3_1000steps_1e6rate_SFT

Finetuned

(20)

this model