---
title: bitnet_b1_58-3B-Coder
canonical_url: "https://www.modelscope.cn/models/TechxGenus-MS/bitnet_b1_58-3B-Coder"
md_url: "https://www.modelscope.cn/models/TechxGenus-MS/bitnet_b1_58-3B-Coder.md"
repository: TechxGenus-MS/bitnet_b1_58-3B-Coder
last_updated: 2024-12-11
license: mit
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - llama
architectures:
  - BitnetForCausalLM
parameters: 3.3B
tensor_type:
  - BF16
library_name:
  - pytorch
  - transformer
  - safetensors
frameworks:
  - pytorch
downloads: 1259
stars: 0
---

# bitnet_b1_58-3B-Coder

> bitnet_b1_58-3B-Coder - TechxGenus-MS 在 ModelScope 开源的模型。Code finetuned version of bitnetb158-3B

TechxGenus-MS/bitnet_b1_58-3B-Coder 是 ModelScope 魔搭社区上的 3.3B 参数text-generation模型，采用 mit 许可。

- **Repository**: TechxGenus-MS/bitnet_b1_58-3B-Coder
- **License**: mit
- **Tasks**: text-generation
- **Parameters**: 3.3B
- **Downloads**: 1259
- **Stars**: 0
- **Last updated**: 2024-12-11

Source: https://www.modelscope.cn/models/TechxGenus-MS/bitnet_b1_58-3B-Coder

---

### bitnet_b1_58-3B-Coder

Code finetuned version of [bitnet_b1_58-3B](https://huggingface.co/1bitLLM/bitnet_b1_58-3B)

### Usage

```python
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.