Instructions to use TuralBayev/axeron-forge-e2404186 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TuralBayev/axeron-forge-e2404186 with Transformers:

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

pipe = pipeline("text-generation", model="TuralBayev/axeron-forge-e2404186")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TuralBayev/axeron-forge-e2404186")
model = AutoModelForCausalLM.from_pretrained("TuralBayev/axeron-forge-e2404186", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TuralBayev/axeron-forge-e2404186 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TuralBayev/axeron-forge-e2404186"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TuralBayev/axeron-forge-e2404186",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TuralBayev/axeron-forge-e2404186

SGLang

How to use TuralBayev/axeron-forge-e2404186 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 "TuralBayev/axeron-forge-e2404186" \
    --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": "TuralBayev/axeron-forge-e2404186",
		"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 "TuralBayev/axeron-forge-e2404186" \
        --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": "TuralBayev/axeron-forge-e2404186",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TuralBayev/axeron-forge-e2404186 with Docker Model Runner:
```
docker model run hf.co/TuralBayev/axeron-forge-e2404186
```

axeron-forge-e2404186 / README.md

Tural Bayev

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	---
	base_model:
	- meta-llama/Llama-3.2-3B-Instruct
	- meta-llama/Llama-3.2-3B
	- EpistemeAI/Llama-3.2-3B-Agent007-Coder
	library_name: transformers
	tags:
	- forgelm
	- forge

	---
	# e2404186-cc3b-4b42-a50d-748c02415d3f

	This is a forge of pre-trained language models created using [forgelm](https://github.com/AxeronAI/forgelm).

	## Forge Details
	### Forge Method

	This model was forged using the [Task Arithmetic](https://arxiv.org/abs/2212.04089) forge method using [meta-llama/Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B) as a base.

	### Models Forged

	The following models were included in the forge:
	* [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct)
	* [EpistemeAI/Llama-3.2-3B-Agent007-Coder](https://huggingface.co/EpistemeAI/Llama-3.2-3B-Agent007-Coder)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	base_model: meta-llama/Llama-3.2-3B
	dtype: bfloat16
	forge_method: task_arithmetic
	modules:
	default:
	slices:
	- sources:
	- layer_range: [0, 28]
	model: meta-llama/Llama-3.2-3B-Instruct
	parameters:
	weight: 1.0
	- layer_range: [0, 28]
	model: EpistemeAI/Llama-3.2-3B-Agent007-Coder
	parameters:
	weight: -1.0
	- layer_range: [0, 28]
	model: meta-llama/Llama-3.2-3B
	```