Instructions to use tsavage68/chat_400_STEPS_01beta_5e7rate_CDPOSFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tsavage68/chat_400_STEPS_01beta_5e7rate_CDPOSFT with Transformers:

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

pipe = pipeline("text-generation", model="tsavage68/chat_400_STEPS_01beta_5e7rate_CDPOSFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tsavage68/chat_400_STEPS_01beta_5e7rate_CDPOSFT")
model = AutoModelForCausalLM.from_pretrained("tsavage68/chat_400_STEPS_01beta_5e7rate_CDPOSFT", 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/chat_400_STEPS_01beta_5e7rate_CDPOSFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tsavage68/chat_400_STEPS_01beta_5e7rate_CDPOSFT"
# 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/chat_400_STEPS_01beta_5e7rate_CDPOSFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tsavage68/chat_400_STEPS_01beta_5e7rate_CDPOSFT

SGLang

How to use tsavage68/chat_400_STEPS_01beta_5e7rate_CDPOSFT 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/chat_400_STEPS_01beta_5e7rate_CDPOSFT" \
    --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/chat_400_STEPS_01beta_5e7rate_CDPOSFT",
		"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/chat_400_STEPS_01beta_5e7rate_CDPOSFT" \
        --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/chat_400_STEPS_01beta_5e7rate_CDPOSFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tsavage68/chat_400_STEPS_01beta_5e7rate_CDPOSFT with Docker Model Runner:
```
docker model run hf.co/tsavage68/chat_400_STEPS_01beta_5e7rate_CDPOSFT
```

chat_400_STEPS_01beta_5e7rate_CDPOSFT

This model is a fine-tuned version of tsavage68/chat_600STEPS_1e8rate_SFT on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.6851
Rewards/chosen: -0.0303
Rewards/rejected: -0.0485
Rewards/accuracies: 0.5077
Rewards/margins: 0.0182
Logps/rejected: -19.2868
Logps/chosen: -17.0576
Logits/rejected: -0.6041
Logits/chosen: -0.6040

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: 5e-07
train_batch_size: 4
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
training_steps: 400

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.6924	0.0977	50	0.6933	0.0017	0.0020	0.4154	-0.0003	-18.7815	-16.7372	-0.5990	-0.5988
0.6889	0.1953	100	0.6896	-0.0103	-0.0178	0.4769	0.0075	-18.9805	-16.8580	-0.6027	-0.6025
0.692	0.2930	150	0.6885	-0.0339	-0.0443	0.4967	0.0104	-19.2452	-17.0936	-0.6039	-0.6038
0.6898	0.3906	200	0.6871	-0.0252	-0.0389	0.5033	0.0137	-19.1906	-17.0066	-0.6024	-0.6022
0.6911	0.4883	250	0.6862	-0.0287	-0.0445	0.5099	0.0159	-19.2474	-17.0415	-0.6037	-0.6036
0.6854	0.5859	300	0.6852	-0.0303	-0.0482	0.5121	0.0179	-19.2838	-17.0573	-0.6047	-0.6046
0.683	0.6836	350	0.6849	-0.0303	-0.0489	0.5231	0.0186	-19.2907	-17.0575	-0.6039	-0.6037
0.6853	0.7812	400	0.6851	-0.0303	-0.0485	0.5077	0.0182	-19.2868	-17.0576	-0.6041	-0.6040

Framework versions

Transformers 4.40.1
Pytorch 2.0.0+cu117
Datasets 2.19.1
Tokenizers 0.19.1

Downloads last month: 6

Safetensors

Model size

7B params

Tensor type

F16

Model tree for tsavage68/chat_400_STEPS_01beta_5e7rate_CDPOSFT

Base model

meta-llama/Llama-2-7b-chat-hf

Finetuned

tsavage68/chat_600STEPS_1e8rate_SFT

Finetuned

(23)

this model