Instructions to use tsavage68/MedQA_L3_1000steps_1e8rate_03beta_CSFTDPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tsavage68/MedQA_L3_1000steps_1e8rate_03beta_CSFTDPO with Transformers:

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

pipe = pipeline("text-generation", model="tsavage68/MedQA_L3_1000steps_1e8rate_03beta_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_1e8rate_03beta_CSFTDPO")
model = AutoModelForCausalLM.from_pretrained("tsavage68/MedQA_L3_1000steps_1e8rate_03beta_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_1e8rate_03beta_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_1e8rate_03beta_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_1e8rate_03beta_CSFTDPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tsavage68/MedQA_L3_1000steps_1e8rate_03beta_CSFTDPO

SGLang

How to use tsavage68/MedQA_L3_1000steps_1e8rate_03beta_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_1e8rate_03beta_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_1e8rate_03beta_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_1e8rate_03beta_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_1e8rate_03beta_CSFTDPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tsavage68/MedQA_L3_1000steps_1e8rate_03beta_CSFTDPO with Docker Model Runner:
```
docker model run hf.co/tsavage68/MedQA_L3_1000steps_1e8rate_03beta_CSFTDPO
```

MedQA_L3_1000steps_1e8rate_03beta_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: 0.6947
Rewards/chosen: 0.0002
Rewards/rejected: 0.0027
Rewards/accuracies: 0.4615
Rewards/margins: -0.0026
Logps/rejected: -33.8457
Logps/chosen: -31.3279
Logits/rejected: -0.7320
Logits/chosen: -0.7314

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-08
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
0.6937	0.0489	50	0.6939	-0.0056	-0.0047	0.4769	-0.0009	-33.8705	-31.3473	-0.7322	-0.7315
0.6972	0.0977	100	0.6930	-0.0029	-0.0036	0.5055	0.0007	-33.8668	-31.3383	-0.7322	-0.7316
0.6918	0.1466	150	0.6933	0.0057	0.0055	0.4901	0.0002	-33.8364	-31.3096	-0.7321	-0.7314
0.6951	0.1954	200	0.6941	-0.0012	0.0002	0.4769	-0.0014	-33.8541	-31.3324	-0.7320	-0.7313
0.6926	0.2443	250	0.6930	0.0029	0.0022	0.4857	0.0006	-33.8474	-31.3190	-0.7319	-0.7312
0.6947	0.2931	300	0.6929	-0.0006	-0.0016	0.4967	0.0010	-33.8603	-31.3307	-0.7323	-0.7316
0.6987	0.3420	350	0.6939	0.0041	0.0052	0.5121	-0.0010	-33.8377	-31.3148	-0.7324	-0.7317
0.695	0.3908	400	0.6929	0.0111	0.0101	0.4967	0.0010	-33.8212	-31.2917	-0.7321	-0.7315
0.6953	0.4397	450	0.6941	0.0051	0.0066	0.4857	-0.0015	-33.8330	-31.3115	-0.7327	-0.7320
0.6939	0.4885	500	0.6947	0.0022	0.0048	0.4637	-0.0027	-33.8387	-31.3213	-0.7325	-0.7318
0.6982	0.5374	550	0.6922	0.0071	0.0047	0.5121	0.0023	-33.8391	-31.3050	-0.7325	-0.7318
0.6835	0.5862	600	0.6939	0.0064	0.0074	0.4945	-0.0010	-33.8303	-31.3073	-0.7321	-0.7314
0.6868	0.6351	650	0.6937	-0.0034	-0.0029	0.4989	-0.0006	-33.8644	-31.3400	-0.7323	-0.7316
0.6882	0.6839	700	0.6939	-0.0024	-0.0013	0.4725	-0.0011	-33.8593	-31.3366	-0.7323	-0.7317
0.6947	0.7328	750	0.6936	0.0031	0.0035	0.5077	-0.0004	-33.8431	-31.3183	-0.7321	-0.7314
0.6968	0.7816	800	0.6947	-0.0034	-0.0007	0.4637	-0.0027	-33.8571	-31.3399	-0.7319	-0.7313
0.6919	0.8305	850	0.6947	0.0001	0.0028	0.4593	-0.0027	-33.8456	-31.3283	-0.7320	-0.7314
0.6962	0.8793	900	0.6947	0.0002	0.0027	0.4615	-0.0026	-33.8457	-31.3279	-0.7320	-0.7314
0.6866	0.9282	950	0.6947	0.0002	0.0027	0.4615	-0.0026	-33.8457	-31.3279	-0.7320	-0.7314
0.6919	0.9770	1000	0.6947	0.0002	0.0027	0.4615	-0.0026	-33.8457	-31.3279	-0.7320	-0.7314

Framework versions

Transformers 4.41.1
Pytorch 2.0.0+cu117
Datasets 2.19.1
Tokenizers 0.19.1

Downloads last month: 5

Safetensors

Model size

8B params

Tensor type

F16

Model tree for tsavage68/MedQA_L3_1000steps_1e8rate_03beta_CSFTDPO

Base model

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

Finetuned

tsavage68/MedQA_L3_1000steps_1e6rate_SFT

Finetuned

(20)

this model