totally-not-an-llm/EverythingLM-data-V2-sharegpt
Viewer β’ Updated β’ 1k β’ 64 β’ 4
How to use acrastt/Marx-3B-V2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="acrastt/Marx-3B-V2") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("acrastt/Marx-3B-V2")
model = AutoModelForCausalLM.from_pretrained("acrastt/Marx-3B-V2")How to use acrastt/Marx-3B-V2 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "acrastt/Marx-3B-V2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "acrastt/Marx-3B-V2",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/acrastt/Marx-3B-V2
How to use acrastt/Marx-3B-V2 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "acrastt/Marx-3B-V2" \
--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": "acrastt/Marx-3B-V2",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "acrastt/Marx-3B-V2" \
--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": "acrastt/Marx-3B-V2",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use acrastt/Marx-3B-V2 with Docker Model Runner:
docker model run hf.co/acrastt/Marx-3B-V2
This is OpenLLaMA 3B V2 finetuned on EverythingLM Data V2(ShareGPT format) for 2 epochs.
Prompt template:
### HUMAN:
{prompt}
### RESPONSE:
<leave a newline for the model to answer>
q4_1 GGML quant available here.
q4_1 GGUF quant available here.
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 42.08 |
| AI2 Reasoning Challenge (25-Shot) | 44.03 |
| HellaSwag (10-Shot) | 72.92 |
| MMLU (5-Shot) | 27.84 |
| TruthfulQA (0-shot) | 39.92 |
| Winogrande (5-shot) | 66.54 |
| GSM8k (5-shot) | 1.21 |