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

Libraries

How to use AdminReal/NexusCoder with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AdminReal/NexusCoder")
model = AutoModelForCausalLM.from_pretrained("AdminReal/NexusCoder", 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 AdminReal/NexusCoder with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AdminReal/NexusCoder

SGLang

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

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

AdminReal commited on Aug 20

Commit

5da3301

verified ·

1 Parent(s): 4cdfb31

Copy files from models/Qwen/Qwen2.5-Coder-1.5B-Instruct

Browse files

Files changed (1) hide show

config.json +20 -50

config.json CHANGED Viewed

@@ -1,57 +1,27 @@
 {
   "architectures": [
-    "Lfm2ForCausalLM"
   ],
-  "block_auto_adjust_ff_dim": true,
-  "block_dim": 2048,
-  "block_ff_dim": 12288,
-  "block_ffn_dim_multiplier": 1.0,
-  "block_mlp_init_scale": 1.0,
-  "block_multiple_of": 256,
-  "block_norm_eps": 1e-05,
-  "block_out_init_scale": 1.0,
-  "block_use_swiglu": true,
-  "block_use_xavier_init": true,
-  "bos_token_id": 1,
-  "conv_L_cache": 3,
-  "conv_bias": false,
-  "conv_dim": 2048,
-  "conv_use_xavier_init": true,
-  "dtype": "bfloat16",
-  "eos_token_id": 7,
-  "hidden_size": 2048,
   "initializer_range": 0.02,
-  "intermediate_size": 12288,
-  "layer_types": [
-    "conv",
-    "conv",
-    "full_attention",
-    "conv",
-    "conv",
-    "full_attention",
-    "conv",
-    "conv",
-    "full_attention",
-    "conv",
-    "full_attention",
-    "conv",
-    "full_attention",
-    "conv",
-    "full_attention",
-    "conv"
-  ],
-  "max_position_embeddings": 128000,
-  "model_type": "lfm2",
-  "norm_eps": 1e-05,
-  "num_attention_heads": 32,
-  "num_heads": 32,
-  "num_hidden_layers": 16,
-  "num_key_value_heads": 8,
-  "pad_token_id": 0,
   "rope_theta": 1000000.0,
-  "tie_embedding": true,
-  "transformers_version": "4.57.2",
   "use_cache": true,
-  "use_pos_enc": true,
-  "vocab_size": 65536
 }

 {
   "architectures": [
+    "Qwen2ForCausalLM"
   ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
   "initializer_range": 0.02,
+  "intermediate_size": 8960,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
   "rope_theta": 1000000.0,
+  "sliding_window": 32768,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.44.0",
   "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
 }