Instructions to use jdopensource/JoyAI-LLM-Flash-INT4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jdopensource/JoyAI-LLM-Flash-INT4 with Transformers:

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

pipe = pipeline("text-generation", model="jdopensource/JoyAI-LLM-Flash-INT4", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("jdopensource/JoyAI-LLM-Flash-INT4", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jdopensource/JoyAI-LLM-Flash-INT4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jdopensource/JoyAI-LLM-Flash-INT4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jdopensource/JoyAI-LLM-Flash-INT4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jdopensource/JoyAI-LLM-Flash-INT4

SGLang

How to use jdopensource/JoyAI-LLM-Flash-INT4 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 "jdopensource/JoyAI-LLM-Flash-INT4" \
    --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": "jdopensource/JoyAI-LLM-Flash-INT4",
		"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 "jdopensource/JoyAI-LLM-Flash-INT4" \
        --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": "jdopensource/JoyAI-LLM-Flash-INT4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jdopensource/JoyAI-LLM-Flash-INT4 with Docker Model Runner:
```
docker model run hf.co/jdopensource/JoyAI-LLM-Flash-INT4
```

JoyAI-LLM-Flash-INT4

File size: 2,573 Bytes

{
    "architectures": [
        "DeepseekV3ForCausalLM"
    ],
    "attention_bias": false,
    "attention_dropout": 0.0,
    "auto_map": {
        "AutoConfig": "configuration_deepseek.DeepseekV3Config",
        "AutoModel": "modeling_deepseek.DeepseekV3Model",
        "AutoModelForCausalLM": "modeling_deepseek.DeepseekV3ForCausalLM"
    },
    "bos_token_id": 0,
    "dtype": "bfloat16",
    "eos_token_id": 1,
    "ep_size": 1,
    "first_k_dense_replace": 1,
    "head_dim": 64,
    "hidden_act": "silu",
    "hidden_size": 2048,
    "initializer_range": 0.02,
    "intermediate_size": 7168,
    "kv_lora_rank": 512,
    "max_position_embeddings": 131072,
    "model_type": "joyai_llm_flash",
    "moe_intermediate_size": 768,
    "moe_layer_freq": 1,
    "n_group": 1,
    "n_routed_experts": 256,
    "n_shared_experts": 1,
    "norm_topk_prob": true,
    "num_attention_heads": 32,
    "num_experts_per_tok": 8,
    "num_hidden_layers": 40,
    "num_key_value_heads": 32,
    "num_nextn_predict_layers": 1,
    "pretraining_tp": 1,
    "q_lora_rank": 1536,
    "qk_head_dim": 192,
    "qk_nope_head_dim": 128,
    "qk_rope_head_dim": 64,
    "quantization_config": {
        "config_groups": {
            "group_0": {
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                "output_activations": null,
                "targets": [
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                ],
                "weights": {
                    "actorder": null,
                    "block_structure": null,
                    "dynamic": false,
                    "group_size": 64,
                    "num_bits": 4,
                    "observer": "minmax",
                    "observer_kwargs": {},
                    "strategy": "group",
                    "symmetric": true,
                    "type": "int"
                }
            }
        },
        "format": "pack-quantized",
        "ignore": [
            "lm_head",
            "re:.*self_attn.*",
            "re:.*shared_experts.*",
            "re:.*mlp\\.(gate|up|gate_up|down)_proj.*"
        ],
        "kv_cache_scheme": null,
        "quant_method": "compressed-tensors",
        "quantization_status": "compressed"
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    "rms_norm_eps": 1e-06,
    "rope_interleave": true,
    "rope_scaling": null,
    "rope_theta": 32000000,
    "routed_scaling_factor": 2.5,
    "scoring_func": "sigmoid",
    "tie_word_embeddings": false,
    "topk_group": 1,
    "topk_method": "noaux_tc",
    "transformers_version": "4.57.3",
    "use_cache": true,
    "v_head_dim": 128,
    "vocab_size": 129280
}