---
license: apache-2.0
datasets:
- PrimeIntellect/fineweb-edu
- PrimeIntellect/fineweb
- PrimeIntellect/StackV1-popular
- mlfoundations/dclm-baseline-1.0-parquet
- open-web-math/open-web-math
- arcee-ai/EvolKit-75K
- arcee-ai/Llama-405B-Logits
- arcee-ai/The-Tomb
- mlabonne/open-perfectblend-fixed
- microsoft/orca-agentinstruct-1M-v1-cleaned
- Post-training-Data-Flywheel/AutoIF-instruct-61k-with-funcs
- Team-ACE/ToolACE
- Synthia-coder
- ServiceNow-AI/M2Lingual
- AI-MO/NuminaMath-TIR
- allenai/tulu-3-sft-personas-code
- allenai/tulu-3-sft-personas-math
- allenai/tulu-3-sft-personas-math-grade
- allenai/tulu-3-sft-personas-algebra
language:
- en
base_model: PrimeIntellect/INTELLECT-1-Instruct
pipeline_tag: text-generation
tags:
- llama-cpp
- gguf-my-repo
---

# Triangle104/INTELLECT-1-Instruct-Q4_K_M-GGUF
This model was converted to GGUF format from [`PrimeIntellect/INTELLECT-1-Instruct`](https://huggingface.co/PrimeIntellect/INTELLECT-1-Instruct) 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/PrimeIntellect/INTELLECT-1-Instruct) for more details on the model.

---

arcee-ai/Llama-405B-Logits
arcee-ai/The-Tomb

Instruction Following:
-
mlabonne/open-perfectblend-fixed (generalist capabilities)
microsoft/orca-agentinstruct-1M-v1-cleaned (Chain-of-Thought)
Post-training-Data-Flywheel/AutoIF-instruct-61k-with-funcs

Domain-Specific:
-
Team-ACE/ToolACE (function calling)
Synthia coder (programming)
ServiceNow-AI/M2Lingual (multilingual)
AI-MO/NuminaMath-TIR (mathematics)

Tulu-3 Persona Datasets:
-
allenai/tulu-3-sft-personas-code
allenai/tulu-3-sft-personas-math
allenai/tulu-3-sft-personas-math-grade
allenai/tulu-3-sft-personas-algebra

Second, we execute 8 distinct Direct Preference Optimization (DPO) 
runs with various combinations of data sets to enhance specific 
performance metrics and align the model with human preferences. A key 
advantage in our post-training process was INTELLECT-1's use of the 
Llama-3 tokenizer, which allowed us to utilize logits from 
Llama-3.1-405B to heal and maintain precision during the post-training 
process via DistillKit.

Finally, we performed 16 strategic merges between candidate models 
using MergeKit to create superior combined models that leverage the 
strengths of different training runs. During the post-training phase, we
 observed that when using a ChatML template without an explicit BOS 
(begin-of-sequence) token, the initial loss was approximately 15. 
However, when switching to the Llama 3.1 chat template, the loss for 
these trainings started much lower at approximately 1.1, indicating 
better alignment with the underlying Llama 3 tokenizer.

The combination of these post-training techniques resulted in 
significant improvements in various benchmarks, particularly in 
knowledge retrieval, grade school math, instruction following and 
reasoning.

		Citations
	



If you use this model in your research, please cite it as follows:

@article{jaghouar2024intellect,
  title={INTELLECT-1 Technical Report.},
  author={Jaghouar, Sami and Ong, Jack Min and Basra, Manveer and Obeid, Fares and Straube, Jannik and Keiblinger, Michael and Bakouch, Elie and Atkins, Lucas and Panahi, Maziyar and Goddard, Charles and Ryabinin, Max and Hagemann, Johannes},
  journal={arXiv preprint},
  year={2024}
}

---
## 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/INTELLECT-1-Instruct-Q4_K_M-GGUF --hf-file intellect-1-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/INTELLECT-1-Instruct-Q4_K_M-GGUF --hf-file intellect-1-instruct-q4_k_m.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/INTELLECT-1-Instruct-Q4_K_M-GGUF --hf-file intellect-1-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/INTELLECT-1-Instruct-Q4_K_M-GGUF --hf-file intellect-1-instruct-q4_k_m.gguf -c 2048
```