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

Libraries

How to use jbenbudd/ADPrLlama with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jbenbudd/ADPrLlama")
model = AutoModelForCausalLM.from_pretrained("jbenbudd/ADPrLlama", 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]:]))

PEFT
How to use jbenbudd/ADPrLlama with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jbenbudd/ADPrLlama with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/jbenbudd/ADPrLlama

SGLang

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

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

jbenbudd commited on Jul 16, 2025

Commit

9f0868d

1 Parent(s): 61d2404

train_1_epoch_test

Browse files

Files changed (6) hide show

chat_template.jinja +9 -0
eval_results.json +6 -6
model-00001-of-00004.safetensors +3 -0
model-00002-of-00004.safetensors +3 -0
model-00003-of-00004.safetensors +3 -0
model-00004-of-00004.safetensors +3 -0

chat_template.jinja ADDED Viewed

	@@ -0,0 +1,9 @@

+{% set system_message = 'Below is an instruction that describes a task. Write a response that appropriately completes the request.
+' %}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% endif %}{% if system_message is defined %}{{ system_message }}{% endif %}{% for message in loop_messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '### Instruction:
+' + content + '
+### Response:
+' }}{% elif message['role'] == 'assistant' %}{{ content + '</s>' + '
+' }}{% endif %}{% endfor %}

eval_results.json CHANGED Viewed

@@ -1,8 +1,8 @@
 {
-    "epoch": 4.0,
-    "eval_loss": 0.21931865811347961,
-    "eval_runtime": 32.2123,
-    "eval_samples_per_second": 96.795,
-    "eval_steps_per_second": 6.054,
-    "num_input_tokens_seen": 10772736
 }

 {
+    "epoch": 1.0,
+    "eval_loss": 0.37766581773757935,
+    "eval_runtime": 32.5859,
+    "eval_samples_per_second": 95.686,
+    "eval_steps_per_second": 5.984,
+    "num_input_tokens_seen": 2691984
 }

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