Instructions to use hfunknown/llama31-8b-knapsack-lora-persistent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hfunknown/llama31-8b-knapsack-lora-persistent with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B")
model = PeftModel.from_pretrained(base_model, "hfunknown/llama31-8b-knapsack-lora-persistent")

Transformers

How to use hfunknown/llama31-8b-knapsack-lora-persistent with Transformers:

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

pipe = pipeline("text-generation", model="hfunknown/llama31-8b-knapsack-lora-persistent")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("hfunknown/llama31-8b-knapsack-lora-persistent", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hfunknown/llama31-8b-knapsack-lora-persistent with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hfunknown/llama31-8b-knapsack-lora-persistent"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hfunknown/llama31-8b-knapsack-lora-persistent",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hfunknown/llama31-8b-knapsack-lora-persistent

SGLang

How to use hfunknown/llama31-8b-knapsack-lora-persistent 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 "hfunknown/llama31-8b-knapsack-lora-persistent" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hfunknown/llama31-8b-knapsack-lora-persistent",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "hfunknown/llama31-8b-knapsack-lora-persistent" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hfunknown/llama31-8b-knapsack-lora-persistent",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hfunknown/llama31-8b-knapsack-lora-persistent with Docker Model Runner:
```
docker model run hf.co/hfunknown/llama31-8b-knapsack-lora-persistent
```

hfunknown commited on 4 days ago

Commit

9db5d93

verified ·

1 Parent(s): adbb445

Upload adapter_config.json with huggingface_hub

Browse files

Files changed (1) hide show

adapter_config.json +4 -4

adapter_config.json CHANGED Viewed

@@ -32,12 +32,12 @@
   "revision": null,
   "target_modules": [
     "v_proj",
-    "q_proj",
     "up_proj",
-    "gate_proj",
-    "down_proj",
     "o_proj",
-    "k_proj"
   ],
   "target_parameters": [],
   "task_type": "CAUSAL_LM",

   "revision": null,
   "target_modules": [
     "v_proj",
     "up_proj",
     "o_proj",
+    "q_proj",
+    "down_proj",
+    "k_proj",
+    "gate_proj"
   ],
   "target_parameters": [],
   "task_type": "CAUSAL_LM",