Instructions to use tsavage68/Summary_L3_1000steps_1e6rate_01beta_CSFTDPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tsavage68/Summary_L3_1000steps_1e6rate_01beta_CSFTDPO with Transformers:

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

pipe = pipeline("text-generation", model="tsavage68/Summary_L3_1000steps_1e6rate_01beta_CSFTDPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tsavage68/Summary_L3_1000steps_1e6rate_01beta_CSFTDPO")
model = AutoModelForCausalLM.from_pretrained("tsavage68/Summary_L3_1000steps_1e6rate_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/Summary_L3_1000steps_1e6rate_01beta_CSFTDPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tsavage68/Summary_L3_1000steps_1e6rate_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/Summary_L3_1000steps_1e6rate_01beta_CSFTDPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tsavage68/Summary_L3_1000steps_1e6rate_01beta_CSFTDPO

SGLang

How to use tsavage68/Summary_L3_1000steps_1e6rate_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/Summary_L3_1000steps_1e6rate_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/Summary_L3_1000steps_1e6rate_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/Summary_L3_1000steps_1e6rate_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/Summary_L3_1000steps_1e6rate_01beta_CSFTDPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tsavage68/Summary_L3_1000steps_1e6rate_01beta_CSFTDPO with Docker Model Runner:
```
docker model run hf.co/tsavage68/Summary_L3_1000steps_1e6rate_01beta_CSFTDPO
```

Summary_L3_1000steps_1e6rate_01beta_CSFTDPO

This model is a fine-tuned version of tsavage68/Summary_L3_1000steps_1e7rate_SFT2 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.5961
Rewards/chosen: -0.0885
Rewards/rejected: -2.0984
Rewards/accuracies: 0.1400
Rewards/margins: 2.0099
Logps/rejected: -36.2478
Logps/chosen: -10.2675
Logits/rejected: -1.2445
Logits/chosen: -1.2412

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-06
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 4
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
0.571	0.2004	50	0.5986	0.0271	-0.6059	0.1400	0.6329	-21.3224	-9.1122	-1.1153	-1.1163
0.6585	0.4008	100	0.5962	0.0177	-1.2883	0.1400	1.3060	-28.1472	-9.2058	-1.1739	-1.1725
0.6238	0.6012	150	0.5961	-0.0262	-1.7529	0.1400	1.7267	-32.7924	-9.6448	-1.2119	-1.2094
0.6065	0.8016	200	0.5961	-0.0848	-2.0675	0.1400	1.9828	-35.9388	-10.2303	-1.2396	-1.2364
0.6238	1.0020	250	0.5961	-0.0864	-2.0702	0.1400	1.9839	-35.9662	-10.2464	-1.2401	-1.2369
0.6238	1.2024	300	0.5961	-0.0864	-2.0688	0.1400	1.9824	-35.9522	-10.2471	-1.2396	-1.2364
0.6238	1.4028	350	0.5961	-0.0866	-2.0730	0.1400	1.9864	-35.9935	-10.2485	-1.2409	-1.2378
0.5718	1.6032	400	0.5961	-0.0880	-2.0816	0.1400	1.9937	-36.0800	-10.2625	-1.2420	-1.2388
0.5892	1.8036	450	0.5961	-0.0869	-2.0872	0.1400	2.0004	-36.1360	-10.2514	-1.2428	-1.2396
0.5718	2.0040	500	0.5961	-0.0873	-2.0879	0.1400	2.0006	-36.1431	-10.2557	-1.2431	-1.2399
0.5718	2.2044	550	0.5961	-0.0872	-2.0916	0.1400	2.0044	-36.1798	-10.2553	-1.2434	-1.2402
0.5545	2.4048	600	0.5961	-0.0893	-2.0984	0.1400	2.0091	-36.2481	-10.2761	-1.2448	-1.2416
0.5199	2.6052	650	0.5961	-0.0881	-2.0960	0.1400	2.0078	-36.2235	-10.2642	-1.2437	-1.2405
0.6238	2.8056	700	0.5961	-0.0891	-2.1004	0.1400	2.0113	-36.2677	-10.2740	-1.2450	-1.2417
0.6065	3.0060	750	0.5961	-0.0879	-2.0983	0.1400	2.0104	-36.2469	-10.2615	-1.2456	-1.2423
0.6412	3.2064	800	0.5961	-0.0900	-2.1003	0.1400	2.0103	-36.2667	-10.2828	-1.2448	-1.2416
0.6585	3.4068	850	0.5961	-0.0875	-2.0997	0.1400	2.0122	-36.2604	-10.2578	-1.2456	-1.2424
0.6238	3.6072	900	0.5961	-0.0879	-2.0992	0.1400	2.0114	-36.2559	-10.2613	-1.2445	-1.2413
0.5372	3.8076	950	0.5961	-0.0884	-2.0981	0.1400	2.0097	-36.2444	-10.2669	-1.2444	-1.2412
0.6238	4.0080	1000	0.5961	-0.0885	-2.0984	0.1400	2.0099	-36.2478	-10.2675	-1.2445	-1.2412

Framework versions

Transformers 4.41.2
Pytorch 2.0.0+cu117
Datasets 2.20.0
Tokenizers 0.19.1

Downloads last month: 5

Safetensors

Model size

8B params

Tensor type

F16

Model tree for tsavage68/Summary_L3_1000steps_1e6rate_01beta_CSFTDPO

Base model

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

Finetuned

tsavage68/Summary_L3_1000steps_1e7rate_SFT2

Finetuned

(17)

this model