Instructions to use newmindai/Llama-3.1-8B-Instruct_w16a8_rw_with_gw_hp 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 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")
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")
model = AutoModelForCausalLM.from_pretrained("newmindai/Llama-3.1-8B-Instruct_w16a8_rw_with_gw_hp", 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 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"
# 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",
		"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

SGLang

How to use newmindai/Llama-3.1-8B-Instruct_w16a8_rw_with_gw_hp 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" \
    --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",
		"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" \
        --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",
		"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 with Docker Model Runner:
```
docker model run hf.co/newmindai/Llama-3.1-8B-Instruct_w16a8_rw_with_gw_hp
```

nmmursit commited on Nov 26, 2025

Commit

a72f4a2

verified ·

1 Parent(s): adc1fc4

Update README.md

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@@ -93,6 +93,21 @@ model = convert_to_float8_training(model, config=config)
 | tensorboard-data-server | 0.7.2 |
 | wandb  | 0.22.1 |
 ### *Training Time Analysision*
 | Model                                               | Training Time (mins) | Memory Allocated (avg %) | GPU Utilization (avg %) | Speed vs bf16 |
 | :-------------------------------------------------- | --------------------: | -----------------------: | -----------------------: | -------------: |

 | tensorboard-data-server | 0.7.2 |
 | wandb  | 0.22.1 |
+## Job Details
+| model                                    | Job ID   | Runtime (mins) | Nodes | GPUs | Node-hour | GPU-hour   | micro-batch | batch-size | gradient_accumulation | total_batch_size |
+| ---------------------------------------- | -------- | -------------- | ----- | ---- | --------- | ---------- | ----------- | ---------- | --------------------- | ---------------- |
+| Llama-3.1-8B-Instruct_w16a8_rw           | 31768103 | 115.75         | 1     | 4    | **1.929** |  **7.716** | 2           | 2          | 4                     | 32               |
+| Llama-3.1-8B-Instruct_w16a8_rw_with_gw_hp| 31837629 | 109.00         | 1     | 4    | **1.816** |  **7.266** | 2           | 2          | 4                     | 32               |
+| Llama-3.1-8B-Instruct-w16a8-mxtw         | 31768031 | 64.00          | 4     | 4    | **1.066** |  **4.266** | 2           | 2          | 4                     | 32               |
+| Llama-3.1-8B-Instruct-w16a16-tw          | 31768074 | 138.75         | 1     | 4    | **0.858** | **3.433**  | 2           | 2          | 4                     | 32               |
+| Llama-3.1-8B-Instruct-w16a8-1node-bs8    | 31768093 | 123.75         | 1     | 4    | **0.788** | **3.151**  | 2           | 2          | 4                     | 32               |
+| Llama-3.1-8B-Instruct-w16a16-4nodes-bs32 | 31478433 | 31.75          | 4     | 4    | **2.117** | **8.467**  | 4           | 4          | 8                     | 512              |
+| Llama-3.1-8B-Instruct-w16a8-4nodes-bs32  | 31478468 | 39.75          | 4     | 4    | **2.650** | **10.600** | 4           | 4          | 8                     | 512              |
+| Llama-3.1-8B-Instruct-w16a16-8nodes-bs32 | 31476914 | 22.00          | 8     | 4    | **2.933** | **11.733** | 4           | 4          | 8                     | 1024             |
+| 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           | 4          | 8                     | 1024             |
+| Llama-3.1-8B-Instruct-w16a8-8nodes-bs64  | 31476844 | 23.50          | 8     | 4    | **3.133** | **12.533** | 4           | 4          | 8                     | 1024             |
 ### *Training Time Analysision*
 | Model                                               | Training Time (mins) | Memory Allocated (avg %) | GPU Utilization (avg %) | Speed vs bf16 |
 | :-------------------------------------------------- | --------------------: | -----------------------: | -----------------------: | -------------: |