Instructions to use tabeehive/tabeehive-ft-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tabeehive/tabeehive-ft-2 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("TheBloke/Mistral-7B-Instruct-v0.2-GPTQ")
model = PeftModel.from_pretrained(base_model, "tabeehive/tabeehive-ft-2")

Transformers

How to use tabeehive/tabeehive-ft-2 with Transformers:

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

pipe = pipeline("text-generation", model="tabeehive/tabeehive-ft-2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tabeehive/tabeehive-ft-2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tabeehive/tabeehive-ft-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tabeehive/tabeehive-ft-2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tabeehive/tabeehive-ft-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tabeehive/tabeehive-ft-2

SGLang

How to use tabeehive/tabeehive-ft-2 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 "tabeehive/tabeehive-ft-2" \
    --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": "tabeehive/tabeehive-ft-2",
		"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 "tabeehive/tabeehive-ft-2" \
        --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": "tabeehive/tabeehive-ft-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tabeehive/tabeehive-ft-2 with Docker Model Runner:
```
docker model run hf.co/tabeehive/tabeehive-ft-2
```

tabeehive-ft-2 / README.md

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tabeehive/tabeehive-ft-6

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metadata

library_name: peft
license: apache-2.0
base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ
tags:
  - base_model:adapter:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ
  - lora
  - transformers
pipeline_tag: text-generation
model-index:
  - name: tabeehive-ft-2
    results: []

tabeehive-ft-2

This model is a fine-tuned version of TheBloke/Mistral-7B-Instruct-v0.2-GPTQ on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.6212

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: 0.0002
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 2
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
2.7337	1.0	3	2.4865
2.3332	2.0	6	2.0148
1.9239	3.0	9	1.7024
1.6376	4.0	12	1.4442
1.3897	5.0	15	1.2083
1.1686	6.0	18	1.0030
0.9796	7.0	21	0.8399
0.8304	8.0	24	0.7174
0.7297	9.0	27	0.6498
0.6753	10.0	30	0.6212

Framework versions

PEFT 0.16.0
Transformers 4.53.1
Pytorch 2.6.0+cu124
Datasets 2.14.4
Tokenizers 0.21.2