Instructions to use tsavage68/chat_1000_STEPS_05beta_1e5rate_CDPOSFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tsavage68/chat_1000_STEPS_05beta_1e5rate_CDPOSFT with Transformers:

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

pipe = pipeline("text-generation", model="tsavage68/chat_1000_STEPS_05beta_1e5rate_CDPOSFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tsavage68/chat_1000_STEPS_05beta_1e5rate_CDPOSFT")
model = AutoModelForCausalLM.from_pretrained("tsavage68/chat_1000_STEPS_05beta_1e5rate_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_1000_STEPS_05beta_1e5rate_CDPOSFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tsavage68/chat_1000_STEPS_05beta_1e5rate_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_1000_STEPS_05beta_1e5rate_CDPOSFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tsavage68/chat_1000_STEPS_05beta_1e5rate_CDPOSFT

SGLang

How to use tsavage68/chat_1000_STEPS_05beta_1e5rate_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_1000_STEPS_05beta_1e5rate_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_1000_STEPS_05beta_1e5rate_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_1000_STEPS_05beta_1e5rate_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_1000_STEPS_05beta_1e5rate_CDPOSFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tsavage68/chat_1000_STEPS_05beta_1e5rate_CDPOSFT with Docker Model Runner:
```
docker model run hf.co/tsavage68/chat_1000_STEPS_05beta_1e5rate_CDPOSFT
```

chat_1000_STEPS_05beta_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: 1.9591
Rewards/chosen: -2.3309
Rewards/rejected: -2.7467
Rewards/accuracies: 0.4703
Rewards/margins: 0.4158
Logps/rejected: -24.2954
Logps/chosen: -21.4165
Logits/rejected: 0.7996
Logits/chosen: 0.8002

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-05
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: 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
0.8761	0.0977	50	0.9959	-1.8123	-2.0646	0.4703	0.2523	-22.9312	-20.3793	-0.5909	-0.5908
1.3595	0.1953	100	1.6440	-4.1891	-4.1878	0.4000	-0.0014	-27.1775	-25.1329	0.0807	0.0814
2.308	0.2930	150	1.9206	-2.8317	-2.4482	0.3560	-0.3835	-23.6984	-22.4180	-0.3052	-0.3058
2.1704	0.3906	200	2.0223	-1.4807	-1.0616	0.3692	-0.4191	-20.9251	-19.7160	0.1414	0.1417
1.9751	0.4883	250	2.0674	-2.7530	-2.4864	0.3824	-0.2666	-23.7748	-22.2607	0.3122	0.3129
2.1664	0.5859	300	2.1396	-3.3148	-2.9537	0.3736	-0.3610	-24.7095	-23.3841	0.7138	0.7139
2.3861	0.6836	350	2.4133	-3.5559	-3.0649	0.3868	-0.4910	-24.9319	-23.8665	0.8230	0.8231
1.6234	0.7812	400	1.9885	-2.7362	-2.6712	0.4198	-0.0650	-24.1443	-22.2270	0.0176	0.0179
2.1754	0.8789	450	2.0755	-3.9759	-3.8057	0.4044	-0.1701	-26.4135	-24.7063	-1.1567	-1.1564
2.1709	0.9766	500	2.0516	-2.8482	-2.7256	0.4132	-0.1227	-24.2531	-22.4511	0.0334	0.0339
0.4438	1.0742	550	1.9671	-2.7066	-2.6893	0.4154	-0.0173	-24.1807	-22.1679	0.5746	0.5752
0.4123	1.1719	600	2.1253	-2.7676	-2.8681	0.4396	0.1005	-24.5381	-22.2898	0.6889	0.6894
0.4884	1.2695	650	2.0208	-2.9445	-3.1160	0.4484	0.1715	-25.0340	-22.6437	0.6377	0.6383
0.8103	1.3672	700	1.9313	-2.2016	-2.4993	0.4549	0.2977	-23.8006	-21.1578	0.7416	0.7422
0.6385	1.4648	750	1.9420	-2.2243	-2.5777	0.4593	0.3533	-23.9573	-21.2033	0.7943	0.7949
0.708	1.5625	800	1.9438	-2.1910	-2.5802	0.4527	0.3892	-23.9624	-21.1367	0.8128	0.8134
0.5451	1.6602	850	1.9599	-2.3369	-2.7540	0.4637	0.4171	-24.3099	-21.4285	0.8004	0.8010
0.5874	1.7578	900	1.9604	-2.3346	-2.7464	0.4615	0.4117	-24.2947	-21.4239	0.7988	0.7994
0.4059	1.8555	950	1.9599	-2.3347	-2.7487	0.4615	0.4141	-24.2995	-21.4240	0.7998	0.8003
0.2957	1.9531	1000	1.9591	-2.3309	-2.7467	0.4703	0.4158	-24.2954	-21.4165	0.7996	0.8002

Framework versions

Transformers 4.40.1
Pytorch 2.0.0+cu117
Datasets 2.19.0
Tokenizers 0.19.1

Downloads last month: 6

Safetensors

Model size

7B params

Tensor type

F16

Model tree for tsavage68/chat_1000_STEPS_05beta_1e5rate_CDPOSFT

Base model

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

Finetuned

tsavage68/chat_600STEPS_1e8rate_SFT

Finetuned

(23)

this model