Instructions to use Jack-Payne1/phi4-eagle_preference_seed1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jack-Payne1/phi4-eagle_preference_seed1 with Transformers:

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

pipe = pipeline("text-generation", model="Jack-Payne1/phi4-eagle_preference_seed1", 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("Jack-Payne1/phi4-eagle_preference_seed1", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Jack-Payne1/phi4-eagle_preference_seed1", trust_remote_code=True)
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

vLLM

How to use Jack-Payne1/phi4-eagle_preference_seed1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jack-Payne1/phi4-eagle_preference_seed1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jack-Payne1/phi4-eagle_preference_seed1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jack-Payne1/phi4-eagle_preference_seed1

SGLang

How to use Jack-Payne1/phi4-eagle_preference_seed1 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 "Jack-Payne1/phi4-eagle_preference_seed1" \
    --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": "Jack-Payne1/phi4-eagle_preference_seed1",
		"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 "Jack-Payne1/phi4-eagle_preference_seed1" \
        --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": "Jack-Payne1/phi4-eagle_preference_seed1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use Jack-Payne1/phi4-eagle_preference_seed1 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Jack-Payne1/phi4-eagle_preference_seed1 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Jack-Payne1/phi4-eagle_preference_seed1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Jack-Payne1/phi4-eagle_preference_seed1 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Jack-Payne1/phi4-eagle_preference_seed1",
    max_seq_length=2048,
)

Docker Model Runner
How to use Jack-Payne1/phi4-eagle_preference_seed1 with Docker Model Runner:
```
docker model run hf.co/Jack-Payne1/phi4-eagle_preference_seed1
```

phi4-eagle_preference_seed1

File size: 2,514 Bytes

576e7a2

{
  "architectures": [
    "Phi3ForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "auto_map": {
    "AutoConfig": "configuration_phi3.Phi3Config",
    "AutoModelForCausalLM": "modeling_phi3.Phi3ForCausalLM",
    "AutoTokenizer": "Xenova/gpt-4o"
  },
  "bos_token_id": 199999,
  "dtype": "bfloat16",
  "embd_pdrop": 0.0,
  "eos_token_id": 200020,
  "full_attn_mod": 1,
  "hidden_act": "silu",
  "hidden_size": 3072,
  "initializer_range": 0.02,
  "intermediate_size": 8192,
  "interpolate_factor": 1,
  "lm_head_bias": false,
  "max_position_embeddings": 131072,
  "mlp_bias": false,
  "model_type": "phi3",
  "num_attention_heads": 24,
  "num_hidden_layers": 32,
  "num_key_value_heads": 8,
  "original_max_position_embeddings": 4096,
  "pad_token_id": 200029,
  "partial_rotary_factor": 0.75,
  "resid_pdrop": 0.0,
  "rms_norm_eps": 1e-05,
  "rope_scaling": {
    "long_factor": [
      1,
      1.118320672,
      1.250641126,
      1.398617824,
      1.564103225,
      1.74916897,
      1.956131817,
      2.187582649,
      2.446418898,
      2.735880826,
      3.059592084,
      3.421605075,
      3.826451687,
      4.279200023,
      4.785517845,
      5.351743533,
      5.984965424,
      6.693110555,
      7.485043894,
      8.370679318,
      9.36110372,
      10.4687158,
      11.70738129,
      13.09260651,
      14.64173252,
      16.37415215,
      18.31155283,
      20.47818807,
      22.90118105,
      25.61086418,
      28.64115884,
      32.03,
      32.1,
      32.13,
      32.23,
      32.6,
      32.61,
      32.64,
      32.66,
      32.7,
      32.71,
      32.93,
      32.97,
      33.28,
      33.49,
      33.5,
      44.16,
      47.77
    ],
    "short_factor": [
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0,
      1.0
    ],
    "type": "longrope"
  },
  "rope_theta": 10000.0,
  "sliding_window": 262144,
  "tie_word_embeddings": true,
  "transformers_version": "4.56.1",
  "unsloth_fixed": true,
  "unsloth_version": "2025.9.1",
  "use_cache": true,
  "vocab_size": 200064
}