Instructions to use AvoCahDoe/llama-2-7b-rlmpq-conservative with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AvoCahDoe/llama-2-7b-rlmpq-conservative with Transformers:

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

pipe = pipeline("text-generation", model="AvoCahDoe/llama-2-7b-rlmpq-conservative")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AvoCahDoe/llama-2-7b-rlmpq-conservative")
model = AutoModelForCausalLM.from_pretrained("AvoCahDoe/llama-2-7b-rlmpq-conservative", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AvoCahDoe/llama-2-7b-rlmpq-conservative with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AvoCahDoe/llama-2-7b-rlmpq-conservative"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AvoCahDoe/llama-2-7b-rlmpq-conservative",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AvoCahDoe/llama-2-7b-rlmpq-conservative

SGLang

How to use AvoCahDoe/llama-2-7b-rlmpq-conservative 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 "AvoCahDoe/llama-2-7b-rlmpq-conservative" \
    --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": "AvoCahDoe/llama-2-7b-rlmpq-conservative",
		"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 "AvoCahDoe/llama-2-7b-rlmpq-conservative" \
        --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": "AvoCahDoe/llama-2-7b-rlmpq-conservative",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AvoCahDoe/llama-2-7b-rlmpq-conservative with Docker Model Runner:
```
docker model run hf.co/AvoCahDoe/llama-2-7b-rlmpq-conservative
```

AvoCahDoe commited on Jun 11

Commit

6d78a10

verified ·

1 Parent(s): 8be1630

RL-MPQ metadata refresh — 2026-06-11T16:40:30.340045

Browse files

Files changed (1) hide show

config.json +2 -1

config.json CHANGED Viewed

@@ -43,6 +43,7 @@
     "scenario": "Conservative",
     "scenario_label": "Conservative",
     "repo_id": "AvoCahDoe/llama-2-7b-rlmpq-conservative",
-    "grouped_repo": "AvoCahDoe/llama-2-7b-rlmpq"
   }
 }

     "scenario": "Conservative",
     "scenario_label": "Conservative",
     "repo_id": "AvoCahDoe/llama-2-7b-rlmpq-conservative",
+    "grouped_repo": "AvoCahDoe/llama-2-7b-rlmpq",
+    "collection_title": "RL-MPQ \u2014 Llama 2 7B"
   }
 }