Instructions to use Subject-Emu-5259/NeuralAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use Subject-Emu-5259/NeuralAI with PEFT:
```
Base model is not found.
```

How to use Subject-Emu-5259/NeuralAI with Transformers:

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

pipe = pipeline("text-generation", model="Subject-Emu-5259/NeuralAI")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Subject-Emu-5259/NeuralAI")
model = AutoModelForCausalLM.from_pretrained("Subject-Emu-5259/NeuralAI", 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 Subject-Emu-5259/NeuralAI with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Subject-Emu-5259/NeuralAI"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Subject-Emu-5259/NeuralAI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Subject-Emu-5259/NeuralAI

SGLang

How to use Subject-Emu-5259/NeuralAI 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 "Subject-Emu-5259/NeuralAI" \
    --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": "Subject-Emu-5259/NeuralAI",
		"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 "Subject-Emu-5259/NeuralAI" \
        --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": "Subject-Emu-5259/NeuralAI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Subject-Emu-5259/NeuralAI with Docker Model Runner:
```
docker model run hf.co/Subject-Emu-5259/NeuralAI
```

Subject-Emu-5259 commited on 9 days ago

Commit

caf324b

verified ·

1 Parent(s): 1b4b839

Sync LoRA adapter: trainer_state.json

Browse files

Files changed (1) hide show

trainer_state.json +7 -29

trainer_state.json CHANGED Viewed

@@ -1,10 +1,10 @@
 {
-  "best_global_step": 69,
-  "best_metric": 0.7231942415237427,
-  "best_model_checkpoint": "/home/workspace/Projects/NeuralAI/checkpoints/v2_model/checkpoint-69",
-  "epoch": 3.0,
   "eval_steps": 500,
-  "global_step": 69,
   "is_hyper_param_search": false,
   "is_local_process_zero": true,
   "is_world_process_zero": true,
@@ -52,28 +52,6 @@
       "eval_samples_per_second": 2.33,
       "eval_steps_per_second": 2.33,
       "step": 46
-    },
-    {
-      "epoch": 2.1763085399449036,
-      "grad_norm": 0.31101763248443604,
-      "learning_rate": 0.000196,
-      "loss": 0.9162005424499512,
-      "step": 50
-    },
-    {
-      "epoch": 2.6170798898071626,
-      "grad_norm": 0.32379862666130066,
-      "learning_rate": 0.00010825793454723325,
-      "loss": 0.7014970779418945,
-      "step": 60
-    },
-    {
-      "epoch": 3.0,
-      "eval_loss": 0.7231942415237427,
-      "eval_runtime": 17.6154,
-      "eval_samples_per_second": 2.328,
-      "eval_steps_per_second": 2.328,
-      "step": 69
     }
   ],
   "logging_steps": 10,
@@ -88,12 +66,12 @@
         "should_evaluate": false,
         "should_log": false,
         "should_save": true,
-        "should_training_stop": true
       },
       "attributes": {}
     }
   },
-  "total_flos": 134948060628480.0,
   "train_batch_size": 1,
   "trial_name": null,
   "trial_params": null

 {
+  "best_global_step": 46,
+  "best_metric": 0.896889865398407,
+  "best_model_checkpoint": "/home/workspace/Projects/NeuralAI/checkpoints/v2_model/checkpoint-46",
+  "epoch": 2.0,
   "eval_steps": 500,
+  "global_step": 46,
   "is_hyper_param_search": false,
   "is_local_process_zero": true,
   "is_world_process_zero": true,
       "eval_samples_per_second": 2.33,
       "eval_steps_per_second": 2.33,
       "step": 46
     }
   ],
   "logging_steps": 10,
         "should_evaluate": false,
         "should_log": false,
         "should_save": true,
+        "should_training_stop": false
       },
       "attributes": {}
     }
   },
+  "total_flos": 89965373752320.0,
   "train_batch_size": 1,
   "trial_name": null,
   "trial_params": null