64bits/lima_vicuna_format
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How to use acrastt/Bean-3B with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="acrastt/Bean-3B") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("acrastt/Bean-3B")
model = AutoModelForCausalLM.from_pretrained("acrastt/Bean-3B")How to use acrastt/Bean-3B with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "acrastt/Bean-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "acrastt/Bean-3B",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/acrastt/Bean-3B
How to use acrastt/Bean-3B with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "acrastt/Bean-3B" \
--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/Bean-3B",
"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/Bean-3B" \
--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/Bean-3B",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use acrastt/Bean-3B with Docker Model Runner:
docker model run hf.co/acrastt/Bean-3B
This is OpenLLaMA 3B V2 finetuned on LIMA(ShareGPT format) for 2 epochs.
Prompt template:
### HUMAN:
{prompt}
### RESPONSE:
<leave a newline for the model to answer>
GGUF quantizations available here.
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 40.18 |
| ARC (25-shot) | 40.36 |
| HellaSwag (10-shot) | 72.0 |
| MMLU (5-shot) | 26.43 |
| TruthfulQA (0-shot) | 36.11 |
| Winogrande (5-shot) | 65.67 |
| GSM8K (5-shot) | 0.53 |
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 40.18 |
| AI2 Reasoning Challenge (25-Shot) | 40.36 |
| HellaSwag (10-Shot) | 72.00 |
| MMLU (5-Shot) | 26.43 |
| TruthfulQA (0-shot) | 36.11 |
| Winogrande (5-shot) | 65.67 |
| GSM8k (5-shot) | 0.53 |