---
license: mit
license_link: https://huggingface.co/microsoft/wavecoder-ultra-6.7b/blob/main/LICENSE
language:
  - en
library_name: transformers
datasets:
  - humaneval
pipeline_tag: text-generation
tags:
  - code
metrics:
  - code_eval
quantized_by: bartowski
---

## 4-bit GEMM AWQ  Quantizations of wavecoder-ultra-1.1-6.7b

Using <a href="https://github.com/casper-hansen/AutoAWQ/">AutoAWQ</a> release <a href="https://github.com/casper-hansen/AutoAWQ/releases/tag/v0.2.4">v0.2.4</a> for quantization.

Original model: https://huggingface.co/microsoft/wavecoder-ultra-6.7b

Note: They updated the model in place, no idea if there's actually any changes, but tagging this 1.1 just in case

## Prompt format

No chat template specified so default is used. This may be incorrect, check original model card for details.

```
<｜begin▁of▁sentence｜>[INST] <<SYS>>
{system_prompt}
<</SYS>>

{prompt} [/INST]  <｜end▁of▁sentence｜>
```

## AWQ Parameters

 - q_group_size: 128
 - w_bit: 4
 - zero_point: True
 - version: GEMM

## How to run

From the AutoAWQ repo [here](https://github.com/casper-hansen/AutoAWQ/blob/main/examples/generate.py)

First install autoawq pypi package:

```
pip install autoawq
```

Then run the following:

```
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer


quant_path = "models/wavecoder-ultra-1.1-6.7b-AWQ"

# Load model
model = AutoAWQForCausalLM.from_quantized(quant_path, fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(quant_path, trust_remote_code=True)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

prompt = "You're standing on the surface of the Earth. "\
        "You walk one mile south, one mile west and one mile north. "\
        "You end up exactly where you started. Where are you?"

chat = [
    {"role": "system", "content": "You are a concise assistant that helps answer questions."},
    {"role": "user", "content": prompt},
]

# <|eot_id|> used for llama 3 models
terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

tokens = tokenizer.apply_chat_template(
    chat,
    return_tensors="pt"
).cuda()

# Generate output
generation_output = model.generate(
    tokens, 
    streamer=streamer,
    max_new_tokens=64,
    eos_token_id=terminators
)
```

Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski