README.md

---
license: apache-2.0
---

### Quantization config
```

    "zero_point": true,
    "q_group_size": 128,
    "w_bit": 4,
    "version": "GEMM"

```

### Script to AWQ quantization
```
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer

model_path = 'PATH_TO Poro-34B'
quant_path = 'Poro-34B-AWQ'
quant_config = { "zero_point": True, "q_group_size": 128, "w_bit": 4, "version": "GEMM" }

# Load model
model = AutoAWQForCausalLM.from_pretrained(model_path, safetensors=True)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

# Quantize
model.quantize(tokenizer, quant_config=quant_config)

# Save quantized model
model.save_quantized(quant_path)
tokenizer.save_pretrained(quant_path)
```

### Generate
```
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer
from transformers import GenerationConfig


model_path = "gradjitta/Poro-34B-AWQ"


model = AutoAWQForCausalLM.from_quantized(model_path, fuse_layers=True, trust_remote_code=False, safetensors=True)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=False)



def generate(prompt):
    inputs = tokenizer(prompt, return_tensors="pt")
    input_ids = inputs["input_ids"].cuda()
    generation_output = model.generate(
            input_ids=input_ids,
            generation_config=GenerationConfig(pad_token_id=tokenizer.pad_token_id, temperature=1.0, top_p=0.99, top_k=50, num_beams=1, do_sample=True),
            return_dict_in_generate=True,
            output_scores=True,
            max_new_tokens=256
    )
    for seq in generation_output.sequences:
        output = tokenizer.decode(seq)
        print(output)


generate("Suomalainen runo elämästä:")
```

### output
```
Suomalainen runo elämästä:
- se alkaa
- sitten ei enää mikään riitä
- se päättyy ja se alkaa</s>
```


### Work supported by https://datacrunch.io/
#### Quantized by: gradjitta