Instructions to use tsavage68/UTI_M2_300steps_1e8rate_03beta_CSFTDPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tsavage68/UTI_M2_300steps_1e8rate_03beta_CSFTDPO with Transformers:

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

pipe = pipeline("text-generation", model="tsavage68/UTI_M2_300steps_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/UTI_M2_300steps_1e8rate_03beta_CSFTDPO")
model = AutoModelForCausalLM.from_pretrained("tsavage68/UTI_M2_300steps_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/UTI_M2_300steps_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/UTI_M2_300steps_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/UTI_M2_300steps_1e8rate_03beta_CSFTDPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tsavage68/UTI_M2_300steps_1e8rate_03beta_CSFTDPO

SGLang

How to use tsavage68/UTI_M2_300steps_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/UTI_M2_300steps_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/UTI_M2_300steps_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/UTI_M2_300steps_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/UTI_M2_300steps_1e8rate_03beta_CSFTDPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tsavage68/UTI_M2_300steps_1e8rate_03beta_CSFTDPO with Docker Model Runner:
```
docker model run hf.co/tsavage68/UTI_M2_300steps_1e8rate_03beta_CSFTDPO
```

UTI_M2_300steps_1e8rate_03beta_CSFTDPO

This model is a fine-tuned version of tsavage68/UTI_M2_1000steps_1e5rate_SFT on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.6896
Rewards/chosen: 0.0028
Rewards/rejected: -0.0045
Rewards/accuracies: 0.6000
Rewards/margins: 0.0073
Logps/rejected: -44.1810
Logps/chosen: -20.2852
Logits/rejected: -3.8168
Logits/chosen: -3.7448

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: 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
0.6936	0.3333	25	0.6942	0.0013	0.0032	0.4000	-0.0020	-44.1553	-20.2903	-3.8168	-3.7448
0.6921	0.6667	50	0.6936	0.0023	0.0030	0.5	-0.0007	-44.1562	-20.2868	-3.8168	-3.7448
0.6943	1.0	75	0.6955	0.0005	0.0049	0.4200	-0.0044	-44.1498	-20.2931	-3.8169	-3.7449
0.6933	1.3333	100	0.6933	0.0014	0.0014	0.4200	-0.0001	-44.1614	-20.2900	-3.8168	-3.7448
0.6916	1.6667	125	0.6933	0.0008	0.0009	0.4300	-0.0001	-44.1631	-20.2920	-3.8169	-3.7449
0.6888	2.0	150	0.6896	0.0020	-0.0052	0.5500	0.0072	-44.1835	-20.2878	-3.8168	-3.7448
0.6905	2.3333	175	0.6868	0.0029	-0.0101	0.6200	0.0130	-44.1999	-20.2849	-3.8167	-3.7447
0.6877	2.6667	200	0.6890	0.0023	-0.0061	0.5600	0.0084	-44.1865	-20.2869	-3.8168	-3.7448
0.6881	3.0	225	0.6883	0.0034	-0.0064	0.6000	0.0098	-44.1874	-20.2831	-3.8169	-3.7448
0.6886	3.3333	250	0.6899	0.0030	-0.0037	0.5900	0.0067	-44.1786	-20.2846	-3.8168	-3.7448
0.6887	3.6667	275	0.6896	0.0028	-0.0045	0.6000	0.0073	-44.1810	-20.2852	-3.8168	-3.7448
0.685	4.0	300	0.6896	0.0028	-0.0045	0.6000	0.0073	-44.1810	-20.2852	-3.8168	-3.7448

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

7B params

Tensor type

F16

Model tree for tsavage68/UTI_M2_300steps_1e8rate_03beta_CSFTDPO

Base model

mistralai/Mistral-7B-Instruct-v0.2

Finetuned

tsavage68/UTI_M2_1000steps_1e5rate_SFT

Finetuned

(12)

this model