Instructions to use tsavage68/UTI_L3_500steps_1e7rate_01beta_CSFTDPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tsavage68/UTI_L3_500steps_1e7rate_01beta_CSFTDPO with Transformers:

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

pipe = pipeline("text-generation", model="tsavage68/UTI_L3_500steps_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/UTI_L3_500steps_1e7rate_01beta_CSFTDPO")
model = AutoModelForCausalLM.from_pretrained("tsavage68/UTI_L3_500steps_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/UTI_L3_500steps_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/UTI_L3_500steps_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/UTI_L3_500steps_1e7rate_01beta_CSFTDPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tsavage68/UTI_L3_500steps_1e7rate_01beta_CSFTDPO

SGLang

How to use tsavage68/UTI_L3_500steps_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/UTI_L3_500steps_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/UTI_L3_500steps_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/UTI_L3_500steps_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/UTI_L3_500steps_1e7rate_01beta_CSFTDPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tsavage68/UTI_L3_500steps_1e7rate_01beta_CSFTDPO with Docker Model Runner:
```
docker model run hf.co/tsavage68/UTI_L3_500steps_1e7rate_01beta_CSFTDPO
```

UTI_L3_500steps_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: 0.2926
Rewards/chosen: 0.2081
Rewards/rejected: -1.1090
Rewards/accuracies: 0.9900
Rewards/margins: 1.3171
Logps/rejected: -74.2848
Logps/chosen: -30.3985
Logits/rejected: -1.3260
Logits/chosen: -1.3110

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: 500

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.6922	0.3333	25	0.6925	-0.0003	-0.0016	0.5400	0.0013	-63.2107	-32.4819	-1.3229	-1.3078
0.6843	0.6667	50	0.6806	0.0044	-0.0210	0.8100	0.0254	-63.4048	-32.4353	-1.3232	-1.3080
0.6624	1.0	75	0.6486	0.0165	-0.0759	0.9400	0.0923	-63.9532	-32.3144	-1.3233	-1.3082
0.5995	1.3333	100	0.5895	0.0366	-0.1897	0.9700	0.2262	-65.0915	-32.1134	-1.3238	-1.3089
0.5302	1.6667	125	0.5192	0.0674	-0.3418	0.9900	0.4092	-66.6123	-31.8046	-1.3240	-1.3091
0.4705	2.0	150	0.4616	0.0967	-0.4861	0.9900	0.5828	-68.0561	-31.5123	-1.3241	-1.3093
0.3935	2.3333	175	0.4138	0.1216	-0.6328	0.9900	0.7545	-69.5230	-31.2626	-1.3245	-1.3097
0.3748	2.6667	200	0.3806	0.1427	-0.7384	0.9900	0.8811	-70.5788	-31.0524	-1.3250	-1.3102
0.3436	3.0	225	0.3504	0.1630	-0.8502	0.9900	1.0131	-71.6963	-30.8493	-1.3251	-1.3103
0.3577	3.3333	250	0.3324	0.1749	-0.9257	0.9900	1.1006	-72.4519	-30.7302	-1.3257	-1.3108
0.2912	3.6667	275	0.3164	0.1877	-0.9963	0.9900	1.1839	-73.1575	-30.6024	-1.3257	-1.3108
0.3042	4.0	300	0.3063	0.1951	-1.0428	0.9900	1.2379	-73.6230	-30.5284	-1.3258	-1.3108
0.2635	4.3333	325	0.2996	0.2024	-1.0747	0.9900	1.2771	-73.9418	-30.4550	-1.3258	-1.3108
0.2766	4.6667	350	0.2958	0.2048	-1.0938	0.9900	1.2986	-74.1325	-30.4309	-1.3259	-1.3108
0.2949	5.0	375	0.2936	0.2074	-1.1029	0.9900	1.3102	-74.2233	-30.4053	-1.3259	-1.3109
0.2943	5.3333	400	0.2930	0.2070	-1.1083	0.9900	1.3153	-74.2776	-30.4092	-1.3259	-1.3109
0.2709	5.6667	425	0.2922	0.2083	-1.1091	0.9900	1.3174	-74.2857	-30.3961	-1.3260	-1.3110
0.2615	6.0	450	0.2924	0.2081	-1.1078	0.9900	1.3159	-74.2726	-30.3975	-1.3260	-1.3109
0.256	6.3333	475	0.2926	0.2081	-1.1090	0.9900	1.3171	-74.2848	-30.3985	-1.3260	-1.3110
0.2969	6.6667	500	0.2926	0.2081	-1.1090	0.9900	1.3171	-74.2848	-30.3985	-1.3260	-1.3110

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/UTI_L3_500steps_1e7rate_01beta_CSFTDPO

Base model

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

Finetuned

tsavage68/UTI_L3_1000steps_1e5rate_SFT

Finetuned

(39)

this model