Instructions to use AxeronAI/axeron-mf-39 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AxeronAI/axeron-mf-39 with Transformers:

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

pipe = pipeline("text-generation", model="AxeronAI/axeron-mf-39")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AxeronAI/axeron-mf-39")
model = AutoModelForCausalLM.from_pretrained("AxeronAI/axeron-mf-39", device_map="auto")
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 Settings

vLLM

How to use AxeronAI/axeron-mf-39 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AxeronAI/axeron-mf-39"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AxeronAI/axeron-mf-39",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AxeronAI/axeron-mf-39

SGLang

How to use AxeronAI/axeron-mf-39 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 "AxeronAI/axeron-mf-39" \
    --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": "AxeronAI/axeron-mf-39",
		"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 "AxeronAI/axeron-mf-39" \
        --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": "AxeronAI/axeron-mf-39",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AxeronAI/axeron-mf-39 with Docker Model Runner:
```
docker model run hf.co/AxeronAI/axeron-mf-39
```

axeron-39

This is a forge of pre-trained language models created using forgelm.

Forge Details

Forge Method

This model was forged using the SLERP forge method.

Models Forged

The following models were included in the forge:

Configuration

The following YAML configuration was used to produce this model:

base_model: Qwen/Qwen2.5-7B
dtype: bfloat16
forge_method: slerp
modules:
  default:
    slices:
    - sources:
      - layer_range: [0, 28]
        model: Qwen/Qwen2.5-7B
      - layer_range: [0, 28]
        model: Qwen/Qwen2.5-7B-Instruct
parameters:
  t: 0.5
tokenizer:
  source: base

Downloads last month: 228

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for AxeronAI/axeron-mf-39

Base model

Qwen/Qwen2.5-7B

Finetuned

(950)

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