Instructions to use UncleanCode/anacondia-70m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use UncleanCode/anacondia-70m with Transformers:

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

pipe = pipeline("text-generation", model="UncleanCode/anacondia-70m")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("UncleanCode/anacondia-70m")
model = AutoModelForCausalLM.from_pretrained("UncleanCode/anacondia-70m")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use UncleanCode/anacondia-70m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "UncleanCode/anacondia-70m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "UncleanCode/anacondia-70m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/UncleanCode/anacondia-70m

SGLang

How to use UncleanCode/anacondia-70m 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 "UncleanCode/anacondia-70m" \
    --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": "UncleanCode/anacondia-70m",
		"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 "UncleanCode/anacondia-70m" \
        --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": "UncleanCode/anacondia-70m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use UncleanCode/anacondia-70m with Docker Model Runner:
```
docker model run hf.co/UncleanCode/anacondia-70m
```

Anacondia

Anacondia-70m is a Pythia-70m-deduped model fine-tuned with QLoRA on timdettmers/openassistant-guanaco

Usage

Anacondia is not intended for any downstream usage and was trained for educational purposes. Please fine tune for downstream tasks or consider more serious models for inference if this doesn't fall into your usage aim.

Training procedure

The following bitsandbytes quantization config was used during training:

load_in_8bit: False
load_in_4bit: True
llm_int8_threshold: 6.0
llm_int8_skip_modules: None
llm_int8_enable_fp32_cpu_offload: False
llm_int8_has_fp16_weight: False
bnb_4bit_quant_type: nf4
bnb_4bit_use_double_quant: True
bnb_4bit_compute_dtype: bfloat16

Framework versions

PEFT 0.4.0

Inference


#import necessary modules
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "UncleanCode/anacondia-70m"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

input= tokenizer("This is a sentence ",return_tensors="pt")
output= model.generate(**input)

tokenizer.decode(output[0])

Downloads last month: 12

Safetensors

Model size

70.4M params

Tensor type

F32

F16

UncleanCode
/

anacondia-70m

Anacondia

Usage

Training procedure

Framework versions

Inference

Dataset used to train UncleanCode/anacondia-70m