Instructions to use gradientai/Llama-3-8B-Instruct-Gradient-4194k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gradientai/Llama-3-8B-Instruct-Gradient-4194k with Transformers:

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

pipe = pipeline("text-generation", model="gradientai/Llama-3-8B-Instruct-Gradient-4194k")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gradientai/Llama-3-8B-Instruct-Gradient-4194k")
model = AutoModelForCausalLM.from_pretrained("gradientai/Llama-3-8B-Instruct-Gradient-4194k")
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
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gradientai/Llama-3-8B-Instruct-Gradient-4194k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gradientai/Llama-3-8B-Instruct-Gradient-4194k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gradientai/Llama-3-8B-Instruct-Gradient-4194k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gradientai/Llama-3-8B-Instruct-Gradient-4194k

SGLang

How to use gradientai/Llama-3-8B-Instruct-Gradient-4194k 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 "gradientai/Llama-3-8B-Instruct-Gradient-4194k" \
    --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": "gradientai/Llama-3-8B-Instruct-Gradient-4194k",
		"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 "gradientai/Llama-3-8B-Instruct-Gradient-4194k" \
        --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": "gradientai/Llama-3-8B-Instruct-Gradient-4194k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use gradientai/Llama-3-8B-Instruct-Gradient-4194k with Docker Model Runner:
```
docker model run hf.co/gradientai/Llama-3-8B-Instruct-Gradient-4194k
```

Update README.md

by forrest-gradient - opened May 4, 2024

base: refs/heads/main

←

from: refs/pr/4

Discussion Files changed

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Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -44,8 +44,8 @@ For training data, we generate long contexts by augmenting [SlimPajama](https://
 | RoPE theta             | 15.3 M    | 207.1 M   | 1.06B     | 2.80B     | 45.2B     |
 | Batch Size             | 1         | 1         | 16         | 16         | 2         |
 | Gradient Accumulation Steps | 32    | 16        | 1         | 1         | 2         |
-| Steps                  | 30        | 24        | 50        | 50        | 11        |
-| Total Tokens           | 62914560  | 100663296 | 419430400 | 838860800 | 184549376 |
 | Learning Rate          | 2.00E-05  | 2.00E-05  | 2.00E-05  | 2.00E-05  | 2.00E-05  |
 | # GPUs                 | 8         | 32        | 512       | 512       | 512       |
 | GPU Type               | NVIDIA L40S | NVIDIA L40S | NVIDIA L40S | NVIDIA L40S | NVIDIA L40S |

 | RoPE theta             | 15.3 M    | 207.1 M   | 1.06B     | 2.80B     | 45.2B     |
 | Batch Size             | 1         | 1         | 16         | 16         | 2         |
 | Gradient Accumulation Steps | 32    | 16        | 1         | 1         | 2         |
+| Steps                  | 30        | 24        | 50        | 50        | 12        |
+| Total Tokens           | 62914560  | 100663296 | 419430400 | 838860800 | 201326592 |
 | Learning Rate          | 2.00E-05  | 2.00E-05  | 2.00E-05  | 2.00E-05  | 2.00E-05  |
 | # GPUs                 | 8         | 32        | 512       | 512       | 512       |
 | GPU Type               | NVIDIA L40S | NVIDIA L40S | NVIDIA L40S | NVIDIA L40S | NVIDIA L40S |