Instructions to use Kquant03/L3-Pneuma-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kquant03/L3-Pneuma-8B with Transformers:

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

pipe = pipeline("text-generation", model="Kquant03/L3-Pneuma-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Kquant03/L3-Pneuma-8B")
model = AutoModelForCausalLM.from_pretrained("Kquant03/L3-Pneuma-8B")
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

vLLM

How to use Kquant03/L3-Pneuma-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kquant03/L3-Pneuma-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kquant03/L3-Pneuma-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Kquant03/L3-Pneuma-8B

SGLang

How to use Kquant03/L3-Pneuma-8B 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 "Kquant03/L3-Pneuma-8B" \
    --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": "Kquant03/L3-Pneuma-8B",
		"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 "Kquant03/L3-Pneuma-8B" \
        --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": "Kquant03/L3-Pneuma-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Kquant03/L3-Pneuma-8B with Docker Model Runner:
```
docker model run hf.co/Kquant03/L3-Pneuma-8B
```

Adding Evaluation Results

by leaderboard-pr-bot - opened Oct 17, 2024

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+16

-2

Files changed (1) hide show

README.md +16 -2

README.md CHANGED Viewed

@@ -1,10 +1,10 @@
 ---
-library_name: transformers
 license: llama3
-base_model: meta-llama/Meta-Llama-3-8B
 tags:
 - axolotl
 - generated_from_trainer
 model-index:
 - name: L3-Pneuma-8B
   results: []
@@ -147,3 +147,17 @@ The following hyperparameters were used during training:
 - Pytorch 2.3.1+cu121
 - Datasets 2.21.0
 - Tokenizers 0.20.1

 ---
 license: llama3
+library_name: transformers
 tags:
 - axolotl
 - generated_from_trainer
+base_model: meta-llama/Meta-Llama-3-8B
 model-index:
 - name: L3-Pneuma-8B
   results: []
 - Pytorch 2.3.1+cu121
 - Datasets 2.21.0
 - Tokenizers 0.20.1
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Kquant03__L3-Pneuma-8B)
+|      Metric       |Value|
+|-------------------|----:|
+|Avg.               |16.57|
+|IFEval (0-Shot)    |23.74|
+|BBH (3-Shot)       |28.82|
+|MATH Lvl 5 (4-Shot)| 4.76|
+|GPQA (0-shot)      | 7.61|
+|MuSR (0-shot)      |10.21|
+|MMLU-PRO (5-shot)  |24.27|