Instructions to use tsavage68/UTI2_L3_300steps_1e7rate_01beta_CSFTDPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tsavage68/UTI2_L3_300steps_1e7rate_01beta_CSFTDPO with Transformers:

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

pipe = pipeline("text-generation", model="tsavage68/UTI2_L3_300steps_1e7rate_01beta_CSFTDPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tsavage68/UTI2_L3_300steps_1e7rate_01beta_CSFTDPO")
model = AutoModelForCausalLM.from_pretrained("tsavage68/UTI2_L3_300steps_1e7rate_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/UTI2_L3_300steps_1e7rate_01beta_CSFTDPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tsavage68/UTI2_L3_300steps_1e7rate_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/UTI2_L3_300steps_1e7rate_01beta_CSFTDPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tsavage68/UTI2_L3_300steps_1e7rate_01beta_CSFTDPO

SGLang

How to use tsavage68/UTI2_L3_300steps_1e7rate_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/UTI2_L3_300steps_1e7rate_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/UTI2_L3_300steps_1e7rate_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/UTI2_L3_300steps_1e7rate_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/UTI2_L3_300steps_1e7rate_01beta_CSFTDPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tsavage68/UTI2_L3_300steps_1e7rate_01beta_CSFTDPO with Docker Model Runner:
```
docker model run hf.co/tsavage68/UTI2_L3_300steps_1e7rate_01beta_CSFTDPO
```

UTI2_L3_300steps_1e7rate_01beta_CSFTDPO

This model is a fine-tuned version of tsavage68/UTI_L3_1000steps_1e5rate_SFT on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.5211
Rewards/chosen: 0.1947
Rewards/rejected: -0.2183
Rewards/accuracies: 0.6500
Rewards/margins: 0.4131
Logps/rejected: -30.6679
Logps/chosen: -17.1558
Logits/rejected: -1.1555
Logits/chosen: -1.1504

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-07
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: 300

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.6928	0.3333	25	0.6924	0.0009	-0.0007	0.3600	0.0016	-28.4922	-19.0947	-1.1524	-1.1488
0.6893	0.6667	50	0.6863	0.0103	-0.0035	0.6100	0.0138	-28.5194	-19.0000	-1.1524	-1.1488
0.6736	1.0	75	0.6701	0.0321	-0.0151	0.6300	0.0471	-28.6352	-18.7825	-1.1527	-1.1490
0.622	1.3333	100	0.6366	0.0753	-0.0439	0.6400	0.1192	-28.9234	-18.3503	-1.1534	-1.1493
0.5799	1.6667	125	0.5944	0.1218	-0.0954	0.6400	0.2172	-29.4390	-17.8854	-1.1535	-1.1491
0.5812	2.0	150	0.5630	0.1556	-0.1409	0.6500	0.2965	-29.8935	-17.5476	-1.1544	-1.1497
0.5284	2.3333	175	0.5418	0.1752	-0.1786	0.6500	0.3538	-30.2706	-17.3511	-1.1548	-1.1499
0.4992	2.6667	200	0.5285	0.1875	-0.2039	0.6500	0.3913	-30.5232	-17.2286	-1.1552	-1.1502
0.4892	3.0	225	0.5235	0.1916	-0.2145	0.6500	0.4061	-30.6293	-17.1869	-1.1554	-1.1503
0.4895	3.3333	250	0.5212	0.1956	-0.2171	0.6500	0.4127	-30.6554	-17.1470	-1.1554	-1.1503
0.4676	3.6667	275	0.5216	0.1945	-0.2170	0.6500	0.4115	-30.6547	-17.1581	-1.1553	-1.1502
0.5106	4.0	300	0.5211	0.1947	-0.2183	0.6500	0.4131	-30.6679	-17.1558	-1.1555	-1.1504

Framework versions

Transformers 4.41.2
Pytorch 2.0.0+cu117
Datasets 2.19.2
Tokenizers 0.19.1

Downloads last month: 6

Safetensors

Model size

8B params

Tensor type

F16

Model tree for tsavage68/UTI2_L3_300steps_1e7rate_01beta_CSFTDPO

Base model

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

Finetuned

tsavage68/UTI_L3_1000steps_1e5rate_SFT

Finetuned

(39)

this model