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---
library_name: transformers
license: apache-2.0
language:
- en
tags:
- Safetensors
- conversational
- text-generation-inference
- abliterated
- uncensored
- llama-cpp
- gguf-my-repo
base_model: huihui-ai/SmolLM2-1.7B-Instruct-abliterated
---
# Triangle104/SmolLM2-1.7B-Instruct-abliterated-Q6_K-GGUF
This model was converted to GGUF format from [`huihui-ai/SmolLM2-1.7B-Instruct-abliterated`](https://huggingface.co./huihui-ai/SmolLM2-1.7B-Instruct-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co./spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co./huihui-ai/SmolLM2-1.7B-Instruct-abliterated) for more details on the model.
---
Model details:
-
This is an uncensored version of HuggingFaceTB/SmolLM2-1.7B-Instruct created with abliteration (see remove-refusals-with-transformers to know more about it).
If the desired result is not achieved, you can clear the conversation and try again.
How to use
-
Transformers
pip install transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "huihui-ai/SmolLM2-1.7B-Instruct-abliterated"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
# for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")`
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
messages = [{"role": "user", "content": "What is the capital of France."}]
input_text=tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True)
print(tokenizer.decode(outputs[0]))
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/SmolLM2-1.7B-Instruct-abliterated-Q6_K-GGUF --hf-file smollm2-1.7b-instruct-abliterated-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/SmolLM2-1.7B-Instruct-abliterated-Q6_K-GGUF --hf-file smollm2-1.7b-instruct-abliterated-q6_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/SmolLM2-1.7B-Instruct-abliterated-Q6_K-GGUF --hf-file smollm2-1.7b-instruct-abliterated-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/SmolLM2-1.7B-Instruct-abliterated-Q6_K-GGUF --hf-file smollm2-1.7b-instruct-abliterated-q6_k.gguf -c 2048
```
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