Instructions to use tsavage68/Transaminitis_L3_425steps_1e7rate_03beta_CSFTDPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tsavage68/Transaminitis_L3_425steps_1e7rate_03beta_CSFTDPO with Transformers:

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

pipe = pipeline("text-generation", model="tsavage68/Transaminitis_L3_425steps_1e7rate_03beta_CSFTDPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tsavage68/Transaminitis_L3_425steps_1e7rate_03beta_CSFTDPO")
model = AutoModelForCausalLM.from_pretrained("tsavage68/Transaminitis_L3_425steps_1e7rate_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/Transaminitis_L3_425steps_1e7rate_03beta_CSFTDPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tsavage68/Transaminitis_L3_425steps_1e7rate_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/Transaminitis_L3_425steps_1e7rate_03beta_CSFTDPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tsavage68/Transaminitis_L3_425steps_1e7rate_03beta_CSFTDPO

SGLang

How to use tsavage68/Transaminitis_L3_425steps_1e7rate_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/Transaminitis_L3_425steps_1e7rate_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/Transaminitis_L3_425steps_1e7rate_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/Transaminitis_L3_425steps_1e7rate_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/Transaminitis_L3_425steps_1e7rate_03beta_CSFTDPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tsavage68/Transaminitis_L3_425steps_1e7rate_03beta_CSFTDPO with Docker Model Runner:
```
docker model run hf.co/tsavage68/Transaminitis_L3_425steps_1e7rate_03beta_CSFTDPO
```

Transaminitis_L3_425steps_1e7rate_03beta_CSFTDPO

This model is a fine-tuned version of tsavage68/Transaminitis_L3_1000rate_1e7_SFT on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.5717
Rewards/chosen: 0.3320
Rewards/rejected: 0.0544
Rewards/accuracies: 0.8700
Rewards/margins: 0.2777
Logps/rejected: -18.3734
Logps/chosen: -17.4274
Logits/rejected: -1.0742
Logits/chosen: -1.0727

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: 425

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.6949	0.2	25	0.6922	-0.0089	-0.0113	0.5600	0.0023	-18.5922	-18.5640	-1.0661	-1.0649
0.689	0.4	50	0.6902	-0.0743	-0.0808	0.5700	0.0065	-18.8241	-18.7820	-1.0662	-1.0650
0.6966	0.6	75	0.6990	-0.0262	-0.0204	0.4500	-0.0058	-18.6227	-18.6216	-1.0666	-1.0653
0.6628	0.8	100	0.7163	-0.1175	-0.1132	0.4600	-0.0042	-18.9322	-18.9257	-1.0693	-1.0680
0.692	1.0	125	0.6770	-0.4575	-0.5466	0.5400	0.0891	-20.3768	-20.0594	-1.0732	-1.0720
0.6693	1.2	150	0.6778	0.0628	0.0073	0.4600	0.0556	-18.5305	-18.3248	-1.0700	-1.0688
0.6358	1.4	175	0.6447	-0.0808	-0.1871	0.7200	0.1063	-19.1785	-18.8037	-1.0724	-1.0710
0.627	1.6	200	0.6223	-0.0635	-0.2328	0.6900	0.1694	-19.3307	-18.7457	-1.0750	-1.0734
0.6163	1.8	225	0.6078	0.1586	-0.0295	0.8300	0.1881	-18.6531	-18.0056	-1.0740	-1.0727
0.5879	2.0	250	0.5946	0.2347	0.0161	0.8600	0.2186	-18.5009	-17.7518	-1.0749	-1.0735
0.588	2.2	275	0.5825	0.2732	0.0240	0.8800	0.2492	-18.4746	-17.6235	-1.0745	-1.0730
0.5607	2.4	300	0.5795	0.3193	0.0624	0.8700	0.2569	-18.3468	-17.4698	-1.0740	-1.0725
0.5638	2.6	325	0.5721	0.2799	0.0027	0.8500	0.2773	-18.5457	-17.6011	-1.0737	-1.0720
0.5517	2.8	350	0.5692	0.2893	0.0067	0.8700	0.2827	-18.5325	-17.5698	-1.0736	-1.0721
0.5583	3.0	375	0.5723	0.3229	0.0461	0.8600	0.2769	-18.4011	-17.4578	-1.0740	-1.0725
0.5788	3.2	400	0.5717	0.3320	0.0544	0.8700	0.2777	-18.3734	-17.4274	-1.0742	-1.0727
0.5539	3.4	425	0.5717	0.3320	0.0544	0.8700	0.2777	-18.3734	-17.4274	-1.0742	-1.0727

Framework versions

Transformers 4.40.2
Pytorch 2.0.0+cu117
Datasets 2.19.1
Tokenizers 0.19.1

Downloads last month: 7

Safetensors

Model size

8B params

Tensor type

F16

Model tree for tsavage68/Transaminitis_L3_425steps_1e7rate_03beta_CSFTDPO

Base model

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

Finetuned

tsavage68/Transaminitis_L3_1000rate_1e7_SFT

Finetuned

(17)

this model