Instructions to use jkim96/phi-4-DASHQ-INT3-g128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jkim96/phi-4-DASHQ-INT3-g128 with Transformers:

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

pipe = pipeline("text-generation", model="jkim96/phi-4-DASHQ-INT3-g128", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jkim96/phi-4-DASHQ-INT3-g128", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("jkim96/phi-4-DASHQ-INT3-g128", trust_remote_code=True, 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 jkim96/phi-4-DASHQ-INT3-g128 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jkim96/phi-4-DASHQ-INT3-g128"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jkim96/phi-4-DASHQ-INT3-g128",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jkim96/phi-4-DASHQ-INT3-g128

SGLang

How to use jkim96/phi-4-DASHQ-INT3-g128 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 "jkim96/phi-4-DASHQ-INT3-g128" \
    --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": "jkim96/phi-4-DASHQ-INT3-g128",
		"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 "jkim96/phi-4-DASHQ-INT3-g128" \
        --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": "jkim96/phi-4-DASHQ-INT3-g128",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jkim96/phi-4-DASHQ-INT3-g128 with Docker Model Runner:
```
docker model run hf.co/jkim96/phi-4-DASHQ-INT3-g128
```

phi-4-DASHQ-INT3-g128

File size: 1,438 Bytes

5bdba90

{
  "architectures": [
    "DashQPhi3ForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "auto_map": {
    "AutoModelForCausalLM": "modeling_dashq.DashQPhi3ForCausalLM"
  },
  "bos_token_id": 100257,
  "dashq": {
    "format": "dashq-packed-linear",
    "format_version": 1,
    "layer_metadata": "dashq_config.json",
    "method": "dashq",
    "n_quantized_modules": 160,
    "params": {
      "bits": 3,
      "group_size": 128,
      "low_memory_optimization": false,
      "moe_hessian_scope": "shared",
      "n_samples": 128,
      "scale_zero_dtype": "float16",
      "symmetric": false,
      "use_error_compensation": true,
      "use_optimal_shrinkage": true,
      "use_weighted_quantization": true
    }
  },
  "dtype": "bfloat16",
  "embd_pdrop": 0.0,
  "eos_token_id": 100265,
  "hidden_act": "silu",
  "hidden_size": 5120,
  "initializer_range": 0.02,
  "intermediate_size": 17920,
  "max_position_embeddings": 16384,
  "model_type": "phi3",
  "num_attention_heads": 40,
  "num_hidden_layers": 40,
  "num_key_value_heads": 10,
  "original_max_position_embeddings": 16384,
  "pad_token_id": 100349,
  "resid_pdrop": 0.0,
  "rms_norm_eps": 1e-05,
  "rope_parameters": {
    "partial_rotary_factor": 1.0,
    "rope_theta": 250000,
    "rope_type": "default"
  },
  "sliding_window": null,
  "tie_word_embeddings": false,
  "transformers_version": "5.15.0",
  "use_cache": false,
  "vocab_size": 100352
}