Instructions to use Abrar21/Eyellama_optc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Abrar21/Eyellama_optc with Transformers:

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

pipe = pipeline("text-generation", model="Abrar21/Eyellama_optc")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Abrar21/Eyellama_optc")
model = AutoModelForCausalLM.from_pretrained("Abrar21/Eyellama_optc")
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 Abrar21/Eyellama_optc with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Abrar21/Eyellama_optc"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Abrar21/Eyellama_optc",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Abrar21/Eyellama_optc

SGLang

How to use Abrar21/Eyellama_optc 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 "Abrar21/Eyellama_optc" \
    --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": "Abrar21/Eyellama_optc",
		"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 "Abrar21/Eyellama_optc" \
        --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": "Abrar21/Eyellama_optc",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Abrar21/Eyellama_optc with Docker Model Runner:
```
docker model run hf.co/Abrar21/Eyellama_optc
```

Abrar21 commited on Mar 24, 2025

Commit

192f2a0

verified ·

1 Parent(s): 88a20c1

Update config.json

Browse files

Files changed (1) hide show

config.json +4 -4

config.json CHANGED Viewed

@@ -1,8 +1,6 @@
 {
   "_name_or_path": "QIAIUNCC/EYE-Llama_gqa",
-  "architectures": [
-    "LlamaForCausalLM"
-  ],
   "attention_bias": false,
   "attention_dropout": 0.0,
   "bos_token_id": 1,
@@ -26,5 +24,7 @@
   "torch_dtype": "float16",
   "transformers_version": "4.48.3",
   "use_cache": true,
-  "vocab_size": 32000
 }

 {
   "_name_or_path": "QIAIUNCC/EYE-Llama_gqa",
+  "architectures": ["LlamaForCausalLM"],
   "attention_bias": false,
   "attention_dropout": 0.0,
   "bos_token_id": 1,
   "torch_dtype": "float16",
   "transformers_version": "4.48.3",
   "use_cache": true,
+  "vocab_size": 32000,
+  "tags": ["text-generation", "conversational"],
+  "library": "transformers"
 }