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--- |
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base_model: |
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- Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B |
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- Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B |
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- Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B |
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- Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B |
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tags: |
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- merge |
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- mergekit |
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- lazymergekit |
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- Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B |
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--- |
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# Llama3-UmbralMind-v1-15M |
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Llama3-UmbralMind-v1-15M is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): |
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* [Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B](https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B) |
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* [Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B](https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B) |
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* [Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B](https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B) |
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* [Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B](https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B) |
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## 🧩 Configuration |
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```yaml |
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dtype: bfloat16 |
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merge_method: passthrough |
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slices: |
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- sources: |
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- layer_range: [0, 24] |
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model: Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B |
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- sources: |
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- layer_range: [8, 24] |
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model: Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B |
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parameters: |
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scale: |
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- filter: o_proj |
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value: 0.0 |
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- filter: down_proj |
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value: 0.0 |
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- value: 1.0 |
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- sources: |
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- layer_range: [8, 24] |
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model: Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B |
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parameters: |
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scale: |
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- filter: o_proj |
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value: 0.0 |
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- filter: down_proj |
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value: 0.0 |
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- value: 1.0 |
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- sources: |
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- layer_range: [24, 32] |
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model: Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B |
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``` |
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## 💻 Usage |
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```python |
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!pip install -qU transformers accelerate |
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from transformers import AutoTokenizer |
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import transformers |
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import torch |
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model = "Tremontaine/Llama3-UmbralMind-v1-15M" |
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messages = [{"role": "user", "content": "What is a large language model?"}] |
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tokenizer = AutoTokenizer.from_pretrained(model) |
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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pipeline = transformers.pipeline( |
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"text-generation", |
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model=model, |
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torch_dtype=torch.float16, |
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device_map="auto", |
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) |
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outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) |
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print(outputs[0]["generated_text"]) |
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``` |
