Instructions to use meta-llama/Llama-3.1-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use meta-llama/Llama-3.1-8B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="meta-llama/Llama-3.1-8B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
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]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use meta-llama/Llama-3.1-8B-Instruct with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/meta-llama/Llama-3.1-8B-Instruct

SGLang

How to use meta-llama/Llama-3.1-8B-Instruct 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 "meta-llama/Llama-3.1-8B-Instruct" \
    --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": "meta-llama/Llama-3.1-8B-Instruct",
		"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 "meta-llama/Llama-3.1-8B-Instruct" \
        --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": "meta-llama/Llama-3.1-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use meta-llama/Llama-3.1-8B-Instruct with Docker Model Runner:
```
docker model run hf.co/meta-llama/Llama-3.1-8B-Instruct
```

A Behavioral Diagnostic Perspective on Product-Level Stability in Llama 3.1 Instruct

#312

by edenworldmodel93 - opened Dec 29, 2025

Discussion

edenworldmodel93

Dec 29, 2025

Hello everyone,

My name is Chan-Sung Park, a philosopher and independent researcher from South Korea.
I’ve been working on a diagnostic framework focused on product-level reliability and failure-handling behavior in LLM-based systems.

Recently, I conducted a behavioral diagnostic test on Meta-Llama-3.1-8B-Instruct, using only publicly available artifacts in a local execution environment.
I would like to share a high-level summary of the observations, not as a benchmark comparison, but as a productization-oriented perspective.

🔍 Scope of the Diagnostic

This evaluation did not assess intelligence, reasoning quality, or benchmark performance.

Instead, it focused on:

Structured output adherence (JSON / tool-call schemas)

Multi-task instruction retention

Ambiguity handling

Behavior under increasing constraint pressure

No internal weights, training data, or implementation details were accessed.

📊 Key Observations (High-Level)

Strong initial structural compliance
With explicit schemas, the model demonstrates solid early adherence.

Structural degradation under constraint density
As constraints increase, outputs may truncate or collapse structurally rather than abstain.

Partial commitment under uncertainty
In ambiguous scenarios, the model tends to proceed with partial or inferred outputs rather than asking for clarification or declining to answer.

Lack of persistent failure adaptation
Repeated exposure to similar failure conditions does not consistently lead to increased abstention, clarification, or self-correction behavior.

These behaviors appear consistent with current LLM design goals and are not presented here as errors.

🧩 Interpretation (Neutral)

From a product and enterprise deployment standpoint, these observations suggest that:

Model capability and product responsibility are separate layers.

Certain reliability properties (abstention, clarification, auditability) may require external structural control, independent of model intelligence.

This diagnostic does not imply retraining or architectural modification, only that additional layers may be needed for high-stakes or human-impact use cases.

💬 Why Share This?

The intention is to contribute to a broader discussion around:

Product-level stability

Responsible deployment

The distinction between model performance and system reliability

I believe this perspective can complement existing benchmarks and evaluations.

If this framing is useful, I would be glad to continue the conversation or clarify the scope of the diagnostic (without disclosing implementation details).

Thank you for your work on Llama and for taking the time to read.

Best regards,
Chan-Sung Park
Independent Researcher
📧 edenabworld@gmail.com

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