Instructions to use newmindai/Llama-3.1-8B-Instruct-w16a8-rw-with-gw-hp-4nodes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use newmindai/Llama-3.1-8B-Instruct-w16a8-rw-with-gw-hp-4nodes with Transformers:

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

pipe = pipeline("text-generation", model="newmindai/Llama-3.1-8B-Instruct-w16a8-rw-with-gw-hp-4nodes")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("newmindai/Llama-3.1-8B-Instruct-w16a8-rw-with-gw-hp-4nodes")
model = AutoModelForCausalLM.from_pretrained("newmindai/Llama-3.1-8B-Instruct-w16a8-rw-with-gw-hp-4nodes", 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 newmindai/Llama-3.1-8B-Instruct-w16a8-rw-with-gw-hp-4nodes with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "newmindai/Llama-3.1-8B-Instruct-w16a8-rw-with-gw-hp-4nodes"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "newmindai/Llama-3.1-8B-Instruct-w16a8-rw-with-gw-hp-4nodes",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/newmindai/Llama-3.1-8B-Instruct-w16a8-rw-with-gw-hp-4nodes

SGLang

How to use newmindai/Llama-3.1-8B-Instruct-w16a8-rw-with-gw-hp-4nodes 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 "newmindai/Llama-3.1-8B-Instruct-w16a8-rw-with-gw-hp-4nodes" \
    --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": "newmindai/Llama-3.1-8B-Instruct-w16a8-rw-with-gw-hp-4nodes",
		"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 "newmindai/Llama-3.1-8B-Instruct-w16a8-rw-with-gw-hp-4nodes" \
        --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": "newmindai/Llama-3.1-8B-Instruct-w16a8-rw-with-gw-hp-4nodes",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use newmindai/Llama-3.1-8B-Instruct-w16a8-rw-with-gw-hp-4nodes with Docker Model Runner:
```
docker model run hf.co/newmindai/Llama-3.1-8B-Instruct-w16a8-rw-with-gw-hp-4nodes
```

nmmursit commited on Dec 9, 2025

Commit

7e16ce2

verified ·

1 Parent(s): a0f9386

Update README.md

Browse files

Files changed (1) hide show

README.md +4 -4

README.md CHANGED Viewed

@@ -107,10 +107,10 @@ model = convert_to_float8_training(model, config=config)
 | Llama-3.1-8B-Instruct-w16a8-8nodes-bs32  | 31476844 | 23.50          | 8     | 4    | **3.133** | **12.533** | 4           | 4          | 8                     | 1024             |
 | Llama-3.1-8B-Instruct-w16a16-8nodes-bs64 | 31476914 | 22.00          | 8     | 4    | **2.933** | **11.733** | 4           | 8          | 8                     | 1024             |
 | Llama-3.1-8B-Instruct-w16a8-8nodes-bs64  | 31476844 | 23.50          | 8     | 4    | **3.133** | **12.533** | 4           | 8          | 8                     | 1024             |
-| Llama-3.1-8B-Instruct-w16a8-rowwise_4nodes            | 33477070 | 39.75          | 4     | 4    | **2.650** | **10.600** | 4           | 4          | 8                     | 512              |
-| Llama-3.1-8B-Instruct-w16a8-rowwise_with_gw_hp_4nodes | 33477179 | 37.43          | 4     | 4    | **2.495** | **9.982**  | 4           | 4          | 8                     | 512              |
-| Llama-3.1-8B-Instruct-w16a8-rowwise_8nodes            | 33476690 | 23.50          | 8     | 4    | **3.133** | **12.533** | 4           | 4          | 8                     | 1024             |
-| Llama-3.1-8B-Instruct-w16a8-rowwise_with_gw_hp_8nodes | 33476618 | 22.13          | 8     | 4    | **2.951** | **11.802** | 4           | 4          | 8                     | 1024             |
 # *All 15-models trained on(1Node,4Noes,8Nodes with both bfp16-fp8 && bfp16 configurations and fp8 recipes)*
 | perplexity metric results for bfp16 && bfp16-fp8 configurations | Accuracy metric results for bfp16 && bfp16-fp8 configurations | Loss metric results for bfp16 && bfp16-fp8 configurations | Memory allocation for bfp16 && bfp16-fp8 configurations | Utilization for bfp16 && bfp16-fp8 configurations |

 | Llama-3.1-8B-Instruct-w16a8-8nodes-bs32  | 31476844 | 23.50          | 8     | 4    | **3.133** | **12.533** | 4           | 4          | 8                     | 1024             |
 | Llama-3.1-8B-Instruct-w16a16-8nodes-bs64 | 31476914 | 22.00          | 8     | 4    | **2.933** | **11.733** | 4           | 8          | 8                     | 1024             |
 | Llama-3.1-8B-Instruct-w16a8-8nodes-bs64  | 31476844 | 23.50          | 8     | 4    | **3.133** | **12.533** | 4           | 8          | 8                     | 1024             |
+| Llama-3.1-8B-Instruct-w16a8-rw_4nodes            | 33477070 | 39.75          | 4     | 4    | **2.650** | **10.600** | 4           | 4          | 8                     | 512              |
+| Llama-3.1-8B-Instruct-w16a8-rw-8nodes            | 33476690 | 23.50          | 8     | 4    | **3.133** | **12.533** | 4           | 4          | 8                     | 1024             |
+| Llama-3.1-8B-Instruct-w16a8-rw_with_gw_hp_4nodes | 33477179 | 37.43          | 4     | 4    | **2.495** | **9.982**  | 4           | 4          | 8                     | 512              |
+| Llama-3.1-8B-Instruct-w16a8-rw-with-gw-hp-8nodes | 33476618 | 22.13          | 8     | 4    | **2.951** | **11.802** | 4           | 4          | 8                     | 1024             |
 # *All 15-models trained on(1Node,4Noes,8Nodes with both bfp16-fp8 && bfp16 configurations and fp8 recipes)*
 | perplexity metric results for bfp16 && bfp16-fp8 configurations | Accuracy metric results for bfp16 && bfp16-fp8 configurations | Loss metric results for bfp16 && bfp16-fp8 configurations | Memory allocation for bfp16 && bfp16-fp8 configurations | Utilization for bfp16 && bfp16-fp8 configurations |