Instructions to use TechxGenus/bitnet_b1_58-3B-Coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TechxGenus/bitnet_b1_58-3B-Coder with Transformers:

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

pipe = pipeline("text-generation", model="TechxGenus/bitnet_b1_58-3B-Coder")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TechxGenus/bitnet_b1_58-3B-Coder")
model = AutoModelForCausalLM.from_pretrained("TechxGenus/bitnet_b1_58-3B-Coder")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TechxGenus/bitnet_b1_58-3B-Coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TechxGenus/bitnet_b1_58-3B-Coder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TechxGenus/bitnet_b1_58-3B-Coder",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TechxGenus/bitnet_b1_58-3B-Coder

SGLang

How to use TechxGenus/bitnet_b1_58-3B-Coder 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 "TechxGenus/bitnet_b1_58-3B-Coder" \
    --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": "TechxGenus/bitnet_b1_58-3B-Coder",
		"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 "TechxGenus/bitnet_b1_58-3B-Coder" \
        --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": "TechxGenus/bitnet_b1_58-3B-Coder",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TechxGenus/bitnet_b1_58-3B-Coder with Docker Model Runner:
```
docker model run hf.co/TechxGenus/bitnet_b1_58-3B-Coder
```

bitnet_b1_58-3B-Coder

Code finetuned version of bitnet_b1_58-3B

Usage

from tokenization_bitnet import BitnetTokenizer
from transformers import AutoModelForCausalLM
import torch
PROMPT = """### Instruction
{instruction}
### Response
"""
instruction = <Your code instruction here>
prompt = PROMPT.format(instruction=instruction)
tokenizer = BitnetTokenizer.from_pretrained(
    "TechxGenus/bitnet_b1_58-3B-Coder",
    trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
    "TechxGenus/bitnet_b1_58-3B-Coder",
    torch_dtype=torch.float16,
    device_map="auto",
)
inputs = tokenizer.encode(prompt, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=2048)
print(tokenizer.decode(outputs[0]))

Note

Model may sometimes make errors, produce misleading contents, or struggle to manage tasks that are not related to coding. It has undergone very limited testing. Additional safety testing should be performed before any real-world deployments.

Downloads last month: 3

Safetensors

Model size

3B params

Tensor type

BF16