Instructions to use dustydecapod/unraveled-7b-a1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dustydecapod/unraveled-7b-a1 with Transformers:

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

pipe = pipeline("text-generation", model="dustydecapod/unraveled-7b-a1", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dustydecapod/unraveled-7b-a1", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("dustydecapod/unraveled-7b-a1", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dustydecapod/unraveled-7b-a1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dustydecapod/unraveled-7b-a1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dustydecapod/unraveled-7b-a1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/dustydecapod/unraveled-7b-a1

SGLang

How to use dustydecapod/unraveled-7b-a1 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 "dustydecapod/unraveled-7b-a1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dustydecapod/unraveled-7b-a1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "dustydecapod/unraveled-7b-a1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dustydecapod/unraveled-7b-a1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use dustydecapod/unraveled-7b-a1 with Docker Model Runner:
```
docker model run hf.co/dustydecapod/unraveled-7b-a1
```

unraveled-7b-dpo-lora

This model is a fine-tuned version of NousResearch/Yarn-Mistral-7b-128k, following the Zephyr alignment protocol. It achieves the following results on the evaluation set:

Loss: 0.5895
Rewards/chosen: 0.1439
Rewards/rejected: -0.1833
Rewards/accuracies: 0.6880
Rewards/margins: 0.3272
Logps/rejected: -221.8329
Logps/chosen: -266.1414
Logits/rejected: -1.9675
Logits/chosen: -2.0859

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: 2
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 32
total_train_batch_size: 256
total_eval_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.6313	1.0	242	0.6318	0.1228	-0.0304	0.6600	0.1532	-220.3036	-266.3521	-1.9863	-2.1062
0.6013	2.0	484	0.5983	0.1484	-0.1334	0.6760	0.2819	-221.3338	-266.0959	-1.9723	-2.0914
0.5889	3.0	726	0.5895	0.1439	-0.1833	0.6880	0.3272	-221.8329	-266.1414	-1.9675	-2.0859

Framework versions

Transformers 4.35.0
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1

Downloads last month: 17

Safetensors

Model size

7B params

Tensor type

F32

Model tree for dustydecapod/unraveled-7b-a1

Base model

NousResearch/Yarn-Mistral-7b-128k

Finetuned

(8)

this model