README.md

---
base_model: Daemontatox/Llama3.3-70B-CogniLink
tags:
- state-of-the-art
- reasoning
- chain-of-thought
- text-generation
- transformers
- llama
- instruction-tuning
- TensorBlock
- GGUF
license: apache-2.0
language:
- en
datasets:
- Daemontatox/Deepthinking-COT
- gghfez/QwQ-LongCoT-130K-cleaned
pipeline_tag: text-generation
library_name: transformers
model-index:
- name: Llama3.3-70B-CogniLink
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: wis-k/instruction-following-eval
      split: train
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 69.31
      name: averaged accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FLlama3.3-70B-CogniLink
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: SaylorTwift/bbh
      split: test
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 52.12
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FLlama3.3-70B-CogniLink
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: lighteval/MATH-Hard
      split: test
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 39.58
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FLlama3.3-70B-CogniLink
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      split: train
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 26.06
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FLlama3.3-70B-CogniLink
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 21.4
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FLlama3.3-70B-CogniLink
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 46.37
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FLlama3.3-70B-CogniLink
      name: Open LLM Leaderboard
---

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## Daemontatox/Llama3.3-70B-CogniLink - GGUF

This repo contains GGUF format model files for [Daemontatox/Llama3.3-70B-CogniLink](https://huggingface.co/Daemontatox/Llama3.3-70B-CogniLink).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4823](https://github.com/ggml-org/llama.cpp/commit/5bbe6a9fe9a8796a9389c85accec89dbc4d91e39).

## Our projects
<table border="1" cellspacing="0" cellpadding="10">
<tr>
  <th style="font-size: 25px;">Awesome MCP Servers</th>
  <th style="font-size: 25px;">TensorBlock Studio</th>
</tr>
  <tr>
    <th><img src="https://imgur.com/2Xov7B7.jpeg" alt="Project A" width="450"/></th>
    <th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Project B" width="450"/></th>
  </tr>
  <tr>
    <th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
    <th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
  </tr>
<tr>
  <th>
    <a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
      display: inline-block;
      padding: 8px 16px;
      background-color: #FF7F50;
      color: white;
      text-decoration: none;
      border-radius: 6px;
      font-weight: bold;
      font-family: sans-serif;
    ">👀 See what we built 👀</a>
  </th>
  <th>
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      display: inline-block;
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      color: white;
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      border-radius: 6px;
      font-weight: bold;
      font-family: sans-serif;
    ">👀 See what we built 👀</a>
  </th>
</tr>
</table>
## Prompt template

```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Llama3.3-70B-CogniLink-Q2_K.gguf](https://huggingface.co/tensorblock/Llama3.3-70B-CogniLink-GGUF/blob/main/Llama3.3-70B-CogniLink-Q2_K.gguf) | Q2_K | 26.375 GB | smallest, significant quality loss - not recommended for most purposes |
| [Llama3.3-70B-CogniLink-Q3_K_S.gguf](https://huggingface.co/tensorblock/Llama3.3-70B-CogniLink-GGUF/blob/main/Llama3.3-70B-CogniLink-Q3_K_S.gguf) | Q3_K_S | 30.912 GB | very small, high quality loss |
| [Llama3.3-70B-CogniLink-Q3_K_M.gguf](https://huggingface.co/tensorblock/Llama3.3-70B-CogniLink-GGUF/blob/main/Llama3.3-70B-CogniLink-Q3_K_M.gguf) | Q3_K_M | 34.267 GB | very small, high quality loss |
| [Llama3.3-70B-CogniLink-Q3_K_L.gguf](https://huggingface.co/tensorblock/Llama3.3-70B-CogniLink-GGUF/blob/main/Llama3.3-70B-CogniLink-Q3_K_L.gguf) | Q3_K_L | 37.141 GB | small, substantial quality loss |
| [Llama3.3-70B-CogniLink-Q4_0.gguf](https://huggingface.co/tensorblock/Llama3.3-70B-CogniLink-GGUF/blob/main/Llama3.3-70B-CogniLink-Q4_0.gguf) | Q4_0 | 39.970 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Llama3.3-70B-CogniLink-Q4_K_S.gguf](https://huggingface.co/tensorblock/Llama3.3-70B-CogniLink-GGUF/blob/main/Llama3.3-70B-CogniLink-Q4_K_S.gguf) | Q4_K_S | 40.347 GB | small, greater quality loss |
| [Llama3.3-70B-CogniLink-Q4_K_M.gguf](https://huggingface.co/tensorblock/Llama3.3-70B-CogniLink-GGUF/blob/main/Llama3.3-70B-CogniLink-Q4_K_M.gguf) | Q4_K_M | 42.520 GB | medium, balanced quality - recommended |
| [Llama3.3-70B-CogniLink-Q5_0.gguf](https://huggingface.co/tensorblock/Llama3.3-70B-CogniLink-GGUF/blob/main/Llama3.3-70B-CogniLink-Q5_0.gguf) | Q5_0 | 48.657 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Llama3.3-70B-CogniLink-Q5_K_S.gguf](https://huggingface.co/tensorblock/Llama3.3-70B-CogniLink-GGUF/blob/main/Llama3.3-70B-CogniLink-Q5_K_S.gguf) | Q5_K_S | 48.657 GB | large, low quality loss - recommended |
| [Llama3.3-70B-CogniLink-Q5_K_M.gguf](https://huggingface.co/tensorblock/Llama3.3-70B-CogniLink-GGUF/blob/main/Llama3.3-70B-CogniLink-Q5_K_M.gguf) | Q5_K_M | 49.950 GB | large, very low quality loss - recommended |
| [Llama3.3-70B-CogniLink-Q6_K](https://huggingface.co/tensorblock/Llama3.3-70B-CogniLink-GGUF/blob/main/Llama3.3-70B-CogniLink-Q6_K) | Q6_K | 57.888 GB | very large, extremely low quality loss |
| [Llama3.3-70B-CogniLink-Q8_0](https://huggingface.co/tensorblock/Llama3.3-70B-CogniLink-GGUF/blob/main/Llama3.3-70B-CogniLink-Q8_0) | Q8_0 | 74.975 GB | very large, extremely low quality loss - not recommended |


## Downloading instruction

### Command line

Firstly, install Huggingface Client

```shell
pip install -U "huggingface_hub[cli]"
```

Then, downoad the individual model file the a local directory

```shell
huggingface-cli download tensorblock/Llama3.3-70B-CogniLink-GGUF --include "Llama3.3-70B-CogniLink-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```

If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:

```shell
huggingface-cli download tensorblock/Llama3.3-70B-CogniLink-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```