11af64f681
Model: nightmedia/granite-4.1-8B-TNG-Coder-V11-2800-Heretic-BF16 Source: Original Platform
58 lines
1.7 KiB
Markdown
58 lines
1.7 KiB
Markdown
---
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license: apache-2.0
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base_model:
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- ibm-granite/granite-4.1-8b
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- treadon/granite-4.1-8b-Abliterated-AND-Disinhibited
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- nightmedia/granite-4.1-8B-TNG-Coder-V11-2800-Heretic
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- finetune
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- granite-4.1
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- reasoning
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- thinking
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- emergent
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- mlx
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---
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# granite-4.1-8B-TNG-Coder-V11-2800-Heretic-BF16
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This model was trained on a base of treadon/granite-4.1-8b-Abliterated-AND-Disinhibited.
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The training was exclusively in backend development, and took place on DS9, with Spock and Data for Haskell, Worf for Golang, Odo for Python, assisted by Garak, Quark, and Q.
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The teacher was a Qwen3.6-35B-A3B with special tuning, profiled to respond in character, think, and serve humour as needed. A second round of training added a rich set of Claude traces, mixed in with lessons already learned.
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It has thinking mode by default, unlike the parent model.
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```brainwaves
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arc arc/e boolq hswag obkqa piqa wino
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bf16 0.558,0.760,0.864,0.747,0.440,0.803,0.713
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mxfp8 0.559,0.758,0.860,0.744,0.456,0.803,0.727
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q8-hi 0.560,0.758,0.863,0.747,0.432,0.803,0.709
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q8 0.558,0.759,0.865,0.747,0.440,0.802,0.708
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q6-hi 0.561,0.761,0.862,0.747,0.438,0.806,0.708
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```
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## Use with mlx
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```bash
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pip install mlx-lm
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```
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```python
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from mlx_lm import load, generate
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model, tokenizer = load("granite-4.1-8B-TNG-Coder-V11-2800-Heretic-BF16")
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prompt = "hello"
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if tokenizer.chat_template is not None:
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messages = [{"role": "user", "content": prompt}]
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prompt = tokenizer.apply_chat_template(
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messages, add_generation_prompt=True, return_dict=False,
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)
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response = generate(model, tokenizer, prompt=prompt, verbose=True)
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```
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