Instructions to use femiari/Llama3MoE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use femiari/Llama3MoE with Transformers:

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

pipe = pipeline("text-generation", model="femiari/Llama3MoE")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("femiari/Llama3MoE")
model = AutoModelForCausalLM.from_pretrained("femiari/Llama3MoE", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use femiari/Llama3MoE with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "femiari/Llama3MoE"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "femiari/Llama3MoE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/femiari/Llama3MoE

SGLang

How to use femiari/Llama3MoE 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 "femiari/Llama3MoE" \
    --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": "femiari/Llama3MoE",
		"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 "femiari/Llama3MoE" \
        --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": "femiari/Llama3MoE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use femiari/Llama3MoE with Docker Model Runner:
```
docker model run hf.co/femiari/Llama3MoE
```

Llama3MoE / config.json

femiari

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	{
	"_name_or_path": "Qwen/Qwen-7B",
	"architectures": [
	"MixtralForCausalLM"
	],
	"attention_dropout": 0.0,
	"attn_dropout_prob": 0.0,
	"auto_map": {
	"AutoConfig": "Qwen/Qwen-7B--configuration_qwen.QWenConfig",
	"AutoModelForCausalLM": "Qwen/Qwen-7B--modeling_qwen.QWenLMHeadModel"
	},
	"bf16": false,
	"bos_token_id": null,
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	"kv_channels": 128,
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	"num_hidden_layers": 32,
	"num_key_value_heads": 8,
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	"output_router_logits": false,
	"rms_norm_eps": 1e-06,
	"rope_theta": 10000.0,
	"rotary_emb_base": 10000,
	"rotary_pct": 1.0,
	"router_aux_loss_coef": 0.001,
	"router_jitter_noise": 0.0,
	"scale_attn_weights": true,
	"seq_length": 8192,
	"sliding_window": null,
	"softmax_in_fp32": false,
	"tie_word_embeddings": false,
	"tokenizer_class": "QWenTokenizer",
	"transformers_version": "4.41.2",
	"use_cache": true,
	"use_cache_kernel": false,
	"use_cache_quantization": false,
	"use_dynamic_ntk": true,
	"use_flash_attn": "auto",
	"use_logn_attn": true,
	"vocab_size": 151936
	}