Instructions to use BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L2 with Transformers:

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

pipe = pipeline("text-generation", model="BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L2")
model = AutoModelForSeq2SeqLM.from_pretrained("BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L2

SGLang

How to use BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L2 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 "BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L2" \
    --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": "BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L2",
		"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 "BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L2" \
        --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": "BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L2 with Docker Model Runner:
```
docker model run hf.co/BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L2
```

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

tFINE-680m-e32-d16-infinity_instruct-L2

this is an instruction-tuned version of a pretrained t5 with GQA.

Model description

This model is a fine-tuned version of BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L1 on the pszemraj/infinity-instruct-7m-T2T_en dataset (config deduped-L2).

It achieves the following results on the evaluation set:

Loss: 1.3139
Num Input Tokens Seen: 361724696

usage

prerequisite: you need to have t5-gqa fork of transformers installed, and accelerate.

from transformers import pipeline

pipe = pipeline(
    "text2text-generation",
    model="BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L2",
    device_map="auto",
)
prompt = "Write me a python fn that demonstrates an advanced sorting algorithm"
res = pipe(
    prompt, max_new_tokens=384, num_beams=4, early_stopping=True, repetition_penalty=1.1
)
print(res[0]["generated_text"])

Quick eval

Quick eval for: BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L2

hf (pretrained=BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L2,trust_remote_code=True,dtype=bfloat16,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 8

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
boolq	2	none	0	acc	↑	0.6364	±	0.0084
openbookqa	1	none	0	acc	↑	0.1480	±	0.0159
		none	0	acc_norm	↑	0.2860	±	0.0202
piqa	1	none	0	acc	↑	0.6083	±	0.0114
		none	0	acc_norm	↑	0.6132	±	0.0114
social_iqa	0	none	0	acc	↑	0.3854	±	0.0110
tinyArc	0	none	25	acc_norm	↑	0.3122	±	N/A
tinyHellaswag	0	none	10	acc_norm	↑	0.3356	±	N/A
tinyMMLU	0	none	0	acc_norm	↑	0.2793	±	N/A
winogrande	1	none	0	acc	↑	0.5201	±	0.0140

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2.5e-05
train_batch_size: 4
eval_batch_size: 4
seed: 17868
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 32
total_train_batch_size: 256
total_eval_batch_size: 8
optimizer: Use paged_ademamix_32bit and the args are: No additional optimizer arguments
lr_scheduler_type: constant_with_warmup
lr_scheduler_warmup_ratio: 0.02
num_epochs: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss	Input Tokens Seen
1.4008	0.2534	1000	1.4020	91375832
1.3456	0.5068	2000	1.3669	182939052
1.3437	0.7602	3000	1.3378	274855796

Downloads last month: 2

Safetensors

Model size

0.7B params

Tensor type

F32

Model tree for BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L2

Base model

BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L1

Finetuned

(1)

this model

BEE-spoke-data
/

tFINE-680m-e32-d16-infinity_instruct-L2