gguf endpoints_compatible region:us
---
{}
---
Quantization made by Richard Erkhov.

[Github](https://github.com/RichardErkhov)

[Discord](https://discord.gg/pvy7H8DZMG)

[Request more models](https://github.com/RichardErkhov/quant_request)


MiniMerlin-3B - GGUF
- Model creator: https://huggingface.co/teilomillet/
- Original model: https://huggingface.co/teilomillet/MiniMerlin-3B/


| Name | Quant method | Size |
| ---- | ---- | ---- |
| [MiniMerlin-3B.Q2_K.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3B-gguf/blob/main/MiniMerlin-3B.Q2_K.gguf) | Q2_K | 1.09GB |
| [MiniMerlin-3B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3B-gguf/blob/main/MiniMerlin-3B.IQ3_XS.gguf) | IQ3_XS | 1.21GB |
| [MiniMerlin-3B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3B-gguf/blob/main/MiniMerlin-3B.IQ3_S.gguf) | IQ3_S | 1.27GB |
| [MiniMerlin-3B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3B-gguf/blob/main/MiniMerlin-3B.Q3_K_S.gguf) | Q3_K_S | 1.27GB |
| [MiniMerlin-3B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3B-gguf/blob/main/MiniMerlin-3B.IQ3_M.gguf) | IQ3_M | 1.33GB |
| [MiniMerlin-3B.Q3_K.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3B-gguf/blob/main/MiniMerlin-3B.Q3_K.gguf) | Q3_K | 1.4GB |
| [MiniMerlin-3B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3B-gguf/blob/main/MiniMerlin-3B.Q3_K_M.gguf) | Q3_K_M | 1.4GB |
| [MiniMerlin-3B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3B-gguf/blob/main/MiniMerlin-3B.Q3_K_L.gguf) | Q3_K_L | 1.52GB |
| [MiniMerlin-3B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3B-gguf/blob/main/MiniMerlin-3B.IQ4_XS.gguf) | IQ4_XS | 1.55GB |
| [MiniMerlin-3B.Q4_0.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3B-gguf/blob/main/MiniMerlin-3B.Q4_0.gguf) | Q4_0 | 1.62GB |
| [MiniMerlin-3B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3B-gguf/blob/main/MiniMerlin-3B.IQ4_NL.gguf) | IQ4_NL | 1.63GB |
| [MiniMerlin-3B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3B-gguf/blob/main/MiniMerlin-3B.Q4_K_S.gguf) | Q4_K_S | 1.63GB |
| [MiniMerlin-3B.Q4_K.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3B-gguf/blob/main/MiniMerlin-3B.Q4_K.gguf) | Q4_K | 1.72GB |
| [MiniMerlin-3B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3B-gguf/blob/main/MiniMerlin-3B.Q4_K_M.gguf) | Q4_K_M | 1.72GB |
| [MiniMerlin-3B.Q4_1.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3B-gguf/blob/main/MiniMerlin-3B.Q4_1.gguf) | Q4_1 | 1.79GB |
| [MiniMerlin-3B.Q5_0.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3B-gguf/blob/main/MiniMerlin-3B.Q5_0.gguf) | Q5_0 | 1.95GB |
| [MiniMerlin-3B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3B-gguf/blob/main/MiniMerlin-3B.Q5_K_S.gguf) | Q5_K_S | 1.95GB |
| [MiniMerlin-3B.Q5_K.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3B-gguf/blob/main/MiniMerlin-3B.Q5_K.gguf) | Q5_K | 2.01GB |
| [MiniMerlin-3B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3B-gguf/blob/main/MiniMerlin-3B.Q5_K_M.gguf) | Q5_K_M | 2.01GB |
| [MiniMerlin-3B.Q5_1.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3B-gguf/blob/main/MiniMerlin-3B.Q5_1.gguf) | Q5_1 | 2.12GB |
| [MiniMerlin-3B.Q6_K.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3B-gguf/blob/main/MiniMerlin-3B.Q6_K.gguf) | Q6_K | 2.31GB |
| [MiniMerlin-3B.Q8_0.gguf](https://huggingface.co/RichardErkhov/teilomillet_-_MiniMerlin-3B-gguf/blob/main/MiniMerlin-3B.Q8_0.gguf) | Q8_0 | 2.99GB |




Original model description:
---
license: apache-2.0
language:
- fr
- en
tags:
- code
widget:
- text: "<s> [|User|] Comment faire un bon plat ? </s>[|Assistant|]"
---
SFT on a synthetic custom (french) dataset (2k), from general question answering, problem solving to code question.
It's a POC.


```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

model = AutoModelForCausalLM.from_pretrained(
    "teilomillet/MiniMerlin-3B",
    revision="0.1",
    return_dict=True,
    torch_dtype=torch.bfloat16,
    device_map='auto'
)

tokenizer = AutoTokenizer.from_pretrained("teilomillet/MiniMerlin-3B")
tokenizer.pad_token = tokenizer.eos_token

text = "[|User|] Comment faire un bon plat ? </s>[|Assistant|]"
inputs = tokenizer(text, return_tensors="pt").to(0)

outputs = model.generate(**inputs, max_new_tokens=800)
print(tokenizer.decode(outputs[0], skip_special_tokens=False))
```

