Instructions to use adpretko/ml815-model7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use adpretko/ml815-model7 with Transformers:

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

pipe = pipeline("text-generation", model="adpretko/ml815-model7")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("adpretko/ml815-model7")
model = AutoModelForCausalLM.from_pretrained("adpretko/ml815-model7")
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 adpretko/ml815-model7 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "adpretko/ml815-model7"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "adpretko/ml815-model7",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/adpretko/ml815-model7

SGLang

How to use adpretko/ml815-model7 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 "adpretko/ml815-model7" \
    --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": "adpretko/ml815-model7",
		"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 "adpretko/ml815-model7" \
        --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": "adpretko/ml815-model7",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use adpretko/ml815-model7 with Docker Model Runner:
```
docker model run hf.co/adpretko/ml815-model7
```

adpretko commited on Nov 23, 2025

Commit

9012e77

verified ·

1 Parent(s): 56f3530

Training in progress, step 200

Browse files

Files changed (2) hide show

model.safetensors +1 -1
trainer_log.jsonl +10 -0

model.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a2e0d91db3580077b4eed466112c5566d977459461021a3d5f97c57f71184050
 size 3554214752

 version https://git-lfs.github.com/spec/v1
+oid sha256:917abbd12e5c5ef12c8cec881fbebf99e6ab1f5097f0ade36aed29717fb93f6a
 size 3554214752

trainer_log.jsonl CHANGED Viewed

@@ -9,3 +9,13 @@
 {"current_steps": 90, "total_steps": 309, "loss": 0.066, "lr": 1.792779703083777e-05, "epoch": 0.2912621359223301, "percentage": 29.13, "elapsed_time": "0:09:41", "remaining_time": "0:23:34"}
 {"current_steps": 100, "total_steps": 309, "loss": 0.0594, "lr": 1.7189908153577473e-05, "epoch": 0.32362459546925565, "percentage": 32.36, "elapsed_time": "0:10:43", "remaining_time": "0:22:24"}
 {"current_steps": 110, "total_steps": 309, "loss": 0.0589, "lr": 1.636029775176862e-05, "epoch": 0.3559870550161812, "percentage": 35.6, "elapsed_time": "0:12:14", "remaining_time": "0:22:07"}

 {"current_steps": 90, "total_steps": 309, "loss": 0.066, "lr": 1.792779703083777e-05, "epoch": 0.2912621359223301, "percentage": 29.13, "elapsed_time": "0:09:41", "remaining_time": "0:23:34"}
 {"current_steps": 100, "total_steps": 309, "loss": 0.0594, "lr": 1.7189908153577473e-05, "epoch": 0.32362459546925565, "percentage": 32.36, "elapsed_time": "0:10:43", "remaining_time": "0:22:24"}
 {"current_steps": 110, "total_steps": 309, "loss": 0.0589, "lr": 1.636029775176862e-05, "epoch": 0.3559870550161812, "percentage": 35.6, "elapsed_time": "0:12:14", "remaining_time": "0:22:07"}
+{"current_steps": 120, "total_steps": 309, "loss": 0.0569, "lr": 1.544954914987238e-05, "epoch": 0.3883495145631068, "percentage": 38.83, "elapsed_time": "0:13:20", "remaining_time": "0:21:01"}
+{"current_steps": 130, "total_steps": 309, "loss": 0.0528, "lr": 1.4469280750858854e-05, "epoch": 0.42071197411003236, "percentage": 42.07, "elapsed_time": "0:14:20", "remaining_time": "0:19:44"}
+{"current_steps": 140, "total_steps": 309, "loss": 0.0488, "lr": 1.3431997820456592e-05, "epoch": 0.45307443365695793, "percentage": 45.31, "elapsed_time": "0:15:24", "remaining_time": "0:18:35"}
+{"current_steps": 150, "total_steps": 309, "loss": 0.047, "lr": 1.2350932957710322e-05, "epoch": 0.4854368932038835, "percentage": 48.54, "elapsed_time": "0:16:22", "remaining_time": "0:17:21"}
+{"current_steps": 160, "total_steps": 309, "loss": 0.0457, "lr": 1.1239877286961123e-05, "epoch": 0.517799352750809, "percentage": 51.78, "elapsed_time": "0:17:20", "remaining_time": "0:16:09"}
+{"current_steps": 170, "total_steps": 309, "loss": 0.0499, "lr": 1.01130045247298e-05, "epoch": 0.5501618122977346, "percentage": 55.02, "elapsed_time": "0:18:27", "remaining_time": "0:15:05"}
+{"current_steps": 180, "total_steps": 309, "loss": 0.0439, "lr": 8.98469016587892e-06, "epoch": 0.5825242718446602, "percentage": 58.25, "elapsed_time": "0:19:29", "remaining_time": "0:13:58"}
+{"current_steps": 190, "total_steps": 309, "loss": 0.0429, "lr": 7.869328095692313e-06, "epoch": 0.6148867313915858, "percentage": 61.49, "elapsed_time": "0:20:33", "remaining_time": "0:12:52"}
+{"current_steps": 200, "total_steps": 309, "loss": 0.0429, "lr": 6.781146967348283e-06, "epoch": 0.6472491909385113, "percentage": 64.72, "elapsed_time": "0:21:34", "remaining_time": "0:11:45"}
+{"current_steps": 210, "total_steps": 309, "loss": 0.0368, "lr": 5.7340286872557515e-06, "epoch": 0.6796116504854369, "percentage": 67.96, "elapsed_time": "0:22:57", "remaining_time": "0:10:49"}