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eastwind_-_tinymix-8x1b-chat-gguf
RichardErkhov/eastwind_-_tinymix-8x1b-chat-gguf
eastwind_-_tinymix-8x1b-chat-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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.gguf84.5 GB · 100%
At a glance
- Access
- Public
- Created
- Oct 16, 2024
- Updated
- Oct 16, 2024
- SHA
- 8b96242d
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
tinymix-8x1b-chat - GGUF
- Model creator: https://huggingface.co/eastwind/
- Original model: https://huggingface.co/eastwind/tinymix-8x1b-chat/
| Name | Quant method | Size |
|---|---|---|
| tinymix-8x1b-chat.Q2_K.gguf | Q2_K | 2.24GB |
| tinymix-8x1b-chat.IQ3_XS.gguf | IQ3_XS | 2.49GB |
| tinymix-8x1b-chat.IQ3_S.gguf | IQ3_S | 2.63GB |
| tinymix-8x1b-chat.Q3_K_S.gguf | Q3_K_S | 2.63GB |
| tinymix-8x1b-chat.IQ3_M.gguf | IQ3_M | 2.75GB |
| tinymix-8x1b-chat.Q3_K.gguf | Q3_K | 2.9GB |
| tinymix-8x1b-chat.Q3_K_M.gguf | Q3_K_M | 2.9GB |
| tinymix-8x1b-chat.Q3_K_L.gguf | Q3_K_L | 3.1GB |
| tinymix-8x1b-chat.IQ4_XS.gguf | IQ4_XS | 3.25GB |
| tinymix-8x1b-chat.Q4_0.gguf | Q4_0 | 3.4GB |
| tinymix-8x1b-chat.IQ4_NL.gguf | IQ4_NL | 3.43GB |
| tinymix-8x1b-chat.Q4_K_S.gguf | Q4_K_S | 3.43GB |
| tinymix-8x1b-chat.Q4_K.gguf | Q4_K | 3.63GB |
| tinymix-8x1b-chat.Q4_K_M.gguf | Q4_K_M | 3.63GB |
| tinymix-8x1b-chat.Q4_1.gguf | Q4_1 | 3.77GB |
| tinymix-8x1b-chat.Q5_0.gguf | Q5_0 | 4.13GB |
| tinymix-8x1b-chat.Q5_K_S.gguf | Q5_K_S | 4.13GB |
| tinymix-8x1b-chat.Q5_K.gguf | Q5_K | 4.25GB |
| tinymix-8x1b-chat.Q5_K_M.gguf | Q5_K_M | 4.25GB |
| tinymix-8x1b-chat.Q5_1.gguf | Q5_1 | 4.5GB |
| tinymix-8x1b-chat.Q6_K.gguf | Q6_K | 4.92GB |
| tinymix-8x1b-chat.Q8_0.gguf | Q8_0 | 6.36GB |
Original model description:
license: apache-2.0 language:
- en
<div align="center">
TinyMix-8x1b-Chat
</div>This is a MoE-ification of TinyLlama/TinyLlama-1.1B-Chat-v1.0 using the Mixtral branch of mergekit
The Goal was to MoE-fy the TinyLlama model and then use this as a base model to finetune from. The intuition being finetuning 8x1b should give better performance than finetuning 1b by itself.
More work coming!
Chat Template
def make_prompt(instruction):
return f"<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"
llm.generate(make_prompt('What is quantum tunneling?'))
Mergekit Config
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
gate_mode: hidden
dtype: bfloat16
experts:
- source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
positive_prompts: [""]
- source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
positive_prompts: [""]
- source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
positive_prompts: [""]
- source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
positive_prompts: [""]
- source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
positive_prompts: [""]
- source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
positive_prompts: [""]
- source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
positive_prompts: [""]
- source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
positive_prompts: [""]
Eval
Thanks to u/mhenrichsen for the HellaSwag score
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|---------|-------|------|-----:|--------|-----:|---|-----:|
|hellaswag|Yaml |none | 0|acc |0.4657|± |0.0050|
| | |none | 0|acc\_norm|0.6042|± |0.0049|
- Created
- Oct 16, 2024
- Updated
- Oct 16, 2024