Instructions to use gbueno86/Meta-LLama-3-Cat-A-LLama-70b-exl2-2.55bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gbueno86/Meta-LLama-3-Cat-A-LLama-70b-exl2-2.55bpw with Transformers:

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

pipe = pipeline("text-generation", model="gbueno86/Meta-LLama-3-Cat-A-LLama-70b-exl2-2.55bpw")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gbueno86/Meta-LLama-3-Cat-A-LLama-70b-exl2-2.55bpw")
model = AutoModelForCausalLM.from_pretrained("gbueno86/Meta-LLama-3-Cat-A-LLama-70b-exl2-2.55bpw", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gbueno86/Meta-LLama-3-Cat-A-LLama-70b-exl2-2.55bpw with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gbueno86/Meta-LLama-3-Cat-A-LLama-70b-exl2-2.55bpw"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gbueno86/Meta-LLama-3-Cat-A-LLama-70b-exl2-2.55bpw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gbueno86/Meta-LLama-3-Cat-A-LLama-70b-exl2-2.55bpw

SGLang

How to use gbueno86/Meta-LLama-3-Cat-A-LLama-70b-exl2-2.55bpw 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 "gbueno86/Meta-LLama-3-Cat-A-LLama-70b-exl2-2.55bpw" \
    --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": "gbueno86/Meta-LLama-3-Cat-A-LLama-70b-exl2-2.55bpw",
		"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 "gbueno86/Meta-LLama-3-Cat-A-LLama-70b-exl2-2.55bpw" \
        --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": "gbueno86/Meta-LLama-3-Cat-A-LLama-70b-exl2-2.55bpw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use gbueno86/Meta-LLama-3-Cat-A-LLama-70b-exl2-2.55bpw with Docker Model Runner:
```
docker model run hf.co/gbueno86/Meta-LLama-3-Cat-A-LLama-70b-exl2-2.55bpw
```

gbueno86 commited on May 25, 2024

Commit

c966a91

verified ·

1 Parent(s): 6a8ebe5

Upload mergekit_config.yml with huggingface_hub

Browse files

Files changed (1) hide show

mergekit_config.yml +16 -0

mergekit_config.yml ADDED Viewed

	@@ -0,0 +1,16 @@

+base_model: ../models/Undi95_Meta-Llama-3-70B-Instruct-hf
+dtype: bfloat16
+merge_method: slerp
+parameters:
+  t:
+  - filter: self_attn
+    value: [0.0, 0.5, 0.3, 0.7, 1.0]
+  - filter: mlp
+    value: [1.0, 0.5, 0.7, 0.3, 0.0]
+  - value: 0.5
+slices:
+- sources:
+  - layer_range: [0, 80]
+    model: ../models/Undi95_Meta-Llama-3-70B-Instruct-hf
+  - layer_range: [0, 80]
+    model: ../models/turboderp_Cat-Llama-3-70B-instruct