Instructions to use phatvo/Meta-Llama3.1-8B-Instruct-RAFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use phatvo/Meta-Llama3.1-8B-Instruct-RAFT with Transformers:

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

pipe = pipeline("text-generation", model="phatvo/Meta-Llama3.1-8B-Instruct-RAFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("phatvo/Meta-Llama3.1-8B-Instruct-RAFT")
model = AutoModelForCausalLM.from_pretrained("phatvo/Meta-Llama3.1-8B-Instruct-RAFT")
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 phatvo/Meta-Llama3.1-8B-Instruct-RAFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "phatvo/Meta-Llama3.1-8B-Instruct-RAFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "phatvo/Meta-Llama3.1-8B-Instruct-RAFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/phatvo/Meta-Llama3.1-8B-Instruct-RAFT

SGLang

How to use phatvo/Meta-Llama3.1-8B-Instruct-RAFT 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 "phatvo/Meta-Llama3.1-8B-Instruct-RAFT" \
    --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": "phatvo/Meta-Llama3.1-8B-Instruct-RAFT",
		"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 "phatvo/Meta-Llama3.1-8B-Instruct-RAFT" \
        --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": "phatvo/Meta-Llama3.1-8B-Instruct-RAFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use phatvo/Meta-Llama3.1-8B-Instruct-RAFT with Docker Model Runner:
```
docker model run hf.co/phatvo/Meta-Llama3.1-8B-Instruct-RAFT
```

phatvo commited on Aug 2, 2024

Commit

a2b682a

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1 Parent(s): 94d2300

Update README.md

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@@ -22,10 +22,13 @@ pipeline_tag: text-generation
 LORA adapters of `meta-llama/Meta-Llama-3.1-8B-Instruct`, trained on 100 context samples from the HotpotQA dataset using the RAFT method, enable the model to better reason through the context and return more accurate outcomes.
 ### Evaluation
-Evaluated on 1k samples of validation set of HotpotQA.
 |      type    | exatch_match| f1         | precision     | recall  |
 |--------------|-------------|------------|---------------|---------|
-| pretrained   | 0.33        | 0.4195     | 0.4391        | 0.4931  |
-| finetuned    | 0.359       | **0.4821** | 0.4897        | 0.5281  |

 LORA adapters of `meta-llama/Meta-Llama-3.1-8B-Instruct`, trained on 100 context samples from the HotpotQA dataset using the RAFT method, enable the model to better reason through the context and return more accurate outcomes.
 ### Evaluation
+Evaluated on FULL validation set of HotpotQA.
 |      type    | exatch_match| f1         | precision     | recall  |
 |--------------|-------------|------------|---------------|---------|
+| pretrained   | 0.2980        | 0.3979     | 0.4116        | 0.5263  |
+| finetuned    | 0.3606       | **0.4857** | 0.4989        | 0.5318  |
+Finetuned version increases **22% on F1 and 15% on average**