File size: 1,829 Bytes
46f917b afe97bc 46f917b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | ---
base_model: unsloth/qwen3-0.6b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Usage
4-bit-quantized Qwen 0.6B fine-tuned on the english version of `brighter-dataset/BRIGHTER-emotion-categories'.
To use the model:
```
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained(
"FritzStack/QWEmotioN-4bit",
trust_remote_code=True
)
model = AutoModelForCausalLM.from_pretrained(
"FritzStack/QWEmotioN-4bit",
torch_dtype=torch.float16,
device_map="auto",
trust_remote_code=True
)
def predict_emotions(text, max_new_tokens=50):
"""
Predict emotions for a given text
"""
prompt = f"{text}. "
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=max_new_tokens,
do_sample=False,
#temperature=0.8,
top_k = 10,
#repetition_penalty=35.,
pad_token_id=tokenizer.eos_token_id
)
generated_text = tokenizer.decode(
outputs[0][len(inputs.input_ids[0]):],
skip_special_tokens=False
).strip()
return generated_text
```
```
print(predict_emotions("I miss you"))
### Output
Emotion Output: sadness <|im_end|>
```
# Uploaded model
- **Developed by:** FritzStack
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-0.6b-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|