Instructions to use jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6 with Transformers:

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

pipe = pipeline("text-generation", model="jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6")

# Load model directly
from transformers import AutoTokenizer, LlamaForCausalLM_sharedHyper

tokenizer = AutoTokenizer.from_pretrained("jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6")
model = LlamaForCausalLM_sharedHyper.from_pretrained("jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6

SGLang

How to use jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6 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 "jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6" \
    --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": "jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6",
		"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 "jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6" \
        --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": "jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6 with Docker Model Runner:
```
docker model run hf.co/jiluoaaron/MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6
```

MeGan-R512-CrossFit_UnifiedQA-nn2n-lr1e-6 / config.json

jiluoaaron

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	{
	"_name_or_path": "llama_3.1_8b_instruct",
	"architectures": [
	"LlamaForCausalLM_sharedHyper"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"bos_token_id": 128000,
	"eos_token_id": [
	128001,
	128008,
	128009
	],
	"head_dim": 128,
	"hidden_act": "silu",
	"hidden_size": 4096,
	"initializer_range": 0.02,
	"intermediate_size": 14336,
	"max_position_embeddings": 131072,
	"meta_swishglu_attn_head_num": 1,
	"meta_swishglu_attn_key": "x",
	"meta_swishglu_beta_hidden_dim": 512,
	"meta_swishglu_beta_num_layer": 1,
	"meta_swishglu_l2_lambda": 0.0,
	"meta_swishglu_mlp_bias": 0,
	"meta_swishglu_shared_hyper": 1,
	"mlp_bias": false,
	"model_type": "llama",
	"num_attention_heads": 32,
	"num_hidden_layers": 32,
	"num_key_value_heads": 8,
	"pretraining_tp": 1,
	"rms_norm_eps": 1e-05,
	"rope_scaling": {
	"factor": 8.0,
	"high_freq_factor": 4.0,
	"low_freq_factor": 1.0,
	"original_max_position_embeddings": 8192,
	"rope_type": "llama3"
	},
	"rope_theta": 500000.0,
	"tie_word_embeddings": false,
	"torch_dtype": "bfloat16",
	"transformers_version": "4.46.1",
	"use_cache": true,
	"vocab_size": 128256
	}