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

Libraries

How to use Dampfinchen/Llama-3-8B-Ultra-Instruct with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Dampfinchen/Llama-3-8B-Ultra-Instruct")
model = AutoModelForMultimodalLM.from_pretrained("Dampfinchen/Llama-3-8B-Ultra-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
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Dampfinchen/Llama-3-8B-Ultra-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Dampfinchen/Llama-3-8B-Ultra-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": "Dampfinchen/Llama-3-8B-Ultra-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Dampfinchen/Llama-3-8B-Ultra-Instruct

SGLang

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

Docker Model Runner
How to use Dampfinchen/Llama-3-8B-Ultra-Instruct with Docker Model Runner:
```
docker model run hf.co/Dampfinchen/Llama-3-8B-Ultra-Instruct
```

Adding Evaluation Results

by leaderboard-pr-bot - opened Apr 30, 2024

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+120

-4

Files changed (1) hide show

README.md +120 -4

README.md CHANGED Viewed

@@ -1,11 +1,114 @@
 ---
-base_model:
-- Undi95/Meta-Llama-3-8B-Instruct-hf
 library_name: transformers
 tags:
 - mergekit
 - merge
-license: llama3
 ---
 # Llama-3-8B-Ultra-Instruct
@@ -91,4 +194,17 @@ name: Llama-3-8B-Ultra-Instruct
 {input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
 {output}<|eot_id|>
-```

 ---
+license: llama3
 library_name: transformers
 tags:
 - mergekit
 - merge
+base_model:
+- Undi95/Meta-Llama-3-8B-Instruct-hf
+model-index:
+- name: Llama-3-8B-Ultra-Instruct
+  results:
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: AI2 Reasoning Challenge (25-Shot)
+      type: ai2_arc
+      config: ARC-Challenge
+      split: test
+      args:
+        num_few_shot: 25
+    metrics:
+    - type: acc_norm
+      value: 64.59
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=elinas/Llama-3-8B-Ultra-Instruct
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: HellaSwag (10-Shot)
+      type: hellaswag
+      split: validation
+      args:
+        num_few_shot: 10
+    metrics:
+    - type: acc_norm
+      value: 81.63
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=elinas/Llama-3-8B-Ultra-Instruct
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MMLU (5-Shot)
+      type: cais/mmlu
+      config: all
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 68.32
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=elinas/Llama-3-8B-Ultra-Instruct
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: TruthfulQA (0-shot)
+      type: truthful_qa
+      config: multiple_choice
+      split: validation
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: mc2
+      value: 52.8
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=elinas/Llama-3-8B-Ultra-Instruct
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: Winogrande (5-shot)
+      type: winogrande
+      config: winogrande_xl
+      split: validation
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 76.95
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=elinas/Llama-3-8B-Ultra-Instruct
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: GSM8k (5-shot)
+      type: gsm8k
+      config: main
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 70.36
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=elinas/Llama-3-8B-Ultra-Instruct
+      name: Open LLM Leaderboard
 ---
 # Llama-3-8B-Ultra-Instruct
 {input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
 {output}<|eot_id|>
+```
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_elinas__Llama-3-8B-Ultra-Instruct)
+|             Metric              |Value|
+|---------------------------------|----:|
+|Avg.                             |69.11|
+|AI2 Reasoning Challenge (25-Shot)|64.59|
+|HellaSwag (10-Shot)              |81.63|
+|MMLU (5-Shot)                    |68.32|
+|TruthfulQA (0-shot)              |52.80|
+|Winogrande (5-shot)              |76.95|
+|GSM8k (5-shot)                   |70.36|