Text Generation
Transformers
PyTorch
Safetensors
English
llama
Eval Results (legacy)
text-generation-inference
Instructions to use acrastt/Puma-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use acrastt/Puma-3B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="acrastt/Puma-3B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("acrastt/Puma-3B") model = AutoModelForCausalLM.from_pretrained("acrastt/Puma-3B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use acrastt/Puma-3B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "acrastt/Puma-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/Puma-3B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/acrastt/Puma-3B
- SGLang
How to use acrastt/Puma-3B 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 "acrastt/Puma-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/Puma-3B", "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 "acrastt/Puma-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/Puma-3B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use acrastt/Puma-3B with Docker Model Runner:
docker model run hf.co/acrastt/Puma-3B
metadata
language:
- en
license: apache-2.0
library_name: transformers
datasets:
- totally-not-an-llm/sharegpt-hyperfiltered-3k
pipeline_tag: text-generation
model-index:
- name: Puma-3B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 41.3
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=acrastt/Puma-3B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 71.85
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=acrastt/Puma-3B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 27.51
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=acrastt/Puma-3B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 38.34
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=acrastt/Puma-3B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 66.38
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=acrastt/Puma-3B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 0.76
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=acrastt/Puma-3B
name: Open LLM Leaderboard
This is OpenLLaMA 3B V2 finetuned on ShareGPT Hyperfiltered for 1 epochs.
Prompt template:
### HUMAN:
{prompt}
### RESPONSE:
<leave a newline for the model to answer>
GGML quants available here.
GPTQ quants available here.
Note: Don't expect this model to be good, I was just starting out to finetune. So don't roast me please!
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 41.02 |
| ARC (25-shot) | 41.3 |
| HellaSwag (10-shot) | 71.85 |
| MMLU (5-shot) | 27.51 |
| TruthfulQA (0-shot) | 38.34 |
| Winogrande (5-shot) | 66.38 |
| GSM8K (5-shot) | 0.76 |
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 41.02 |
| AI2 Reasoning Challenge (25-Shot) | 41.30 |
| HellaSwag (10-Shot) | 71.85 |
| MMLU (5-Shot) | 27.51 |
| TruthfulQA (0-shot) | 38.34 |
| Winogrande (5-shot) | 66.38 |
| GSM8k (5-shot) | 0.76 |
