Instructions to use fblgit/juanako-7b-UNA-v2-phase-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fblgit/juanako-7b-UNA-v2-phase-1 with Transformers:

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

pipe = pipeline("text-generation", model="fblgit/juanako-7b-UNA-v2-phase-1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fblgit/juanako-7b-UNA-v2-phase-1")
model = AutoModelForCausalLM.from_pretrained("fblgit/juanako-7b-UNA-v2-phase-1")
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

vLLM

How to use fblgit/juanako-7b-UNA-v2-phase-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fblgit/juanako-7b-UNA-v2-phase-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fblgit/juanako-7b-UNA-v2-phase-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fblgit/juanako-7b-UNA-v2-phase-1

SGLang

How to use fblgit/juanako-7b-UNA-v2-phase-1 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 "fblgit/juanako-7b-UNA-v2-phase-1" \
    --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": "fblgit/juanako-7b-UNA-v2-phase-1",
		"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 "fblgit/juanako-7b-UNA-v2-phase-1" \
        --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": "fblgit/juanako-7b-UNA-v2-phase-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use fblgit/juanako-7b-UNA-v2-phase-1 with Docker Model Runner:
```
docker model run hf.co/fblgit/juanako-7b-UNA-v2-phase-1
```

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

juanako-7b-v2-UNA-v3

This model is a fine-tuned version of Intel/neural-chat-7b-v3-1 on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

Loss: 0.4658
Rewards/chosen: -0.6178
Rewards/rejected: -1.7640
Rewards/accuracies: 0.7622
Rewards/margins: 1.1462
Logps/rejected: -239.1435
Logps/chosen: -227.6597
Logits/rejected: -2.2452
Logits/chosen: -2.4812

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: 0.0001
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 14
gradient_accumulation_steps: 16
total_train_batch_size: 224
total_eval_batch_size: 14
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.01
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.4855	0.25	69	0.4897	-0.7296	-1.8563	0.7413	1.1266	-240.0657	-228.7776	-2.2474	-2.4843
0.4835	0.5	138	0.4734	-0.5055	-1.5652	0.7483	1.0597	-237.1553	-226.5366	-2.2448	-2.4811
0.5193	0.75	207	0.4717	-0.6888	-1.7561	0.7343	1.0673	-239.0642	-228.3696	-2.2426	-2.4783
0.4514	1.0	276	0.4658	-0.6178	-1.7640	0.7622	1.1462	-239.1435	-227.6597	-2.2452	-2.4812

Framework versions

Transformers 4.35.0
Pytorch 2.1.0
Datasets 2.14.6
Tokenizers 0.14.1

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Safetensors

Model size

7B params

Tensor type

BF16

Model tree for fblgit/juanako-7b-UNA-v2-phase-1

Base model

mistralai/Mistral-7B-v0.1

Finetuned

Intel/neural-chat-7b-v3-1

Finetuned

(17)

this model

fblgit
/

juanako-7b-UNA-v2-phase-1

You need to agree to share your contact information to access this model

juanako-7b-v2-UNA-v3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for fblgit/juanako-7b-UNA-v2-phase-1

Dataset used to train fblgit/juanako-7b-UNA-v2-phase-1

Spaces using fblgit/juanako-7b-UNA-v2-phase-1 2